Introduction to the MJO
The tropical atmosphere-ocean system varies on many time scales,
including:
- The diurnal or daily cycle, forced by the daily cycle of the sun
due to the rotation of the Earth about its axis
- Synoptic weather systems (e.g., tropical cyclones, hurricanes,
typhoons, and others) that last up to a few days
- Intraseasonal or month-to-month variability (the Madden-Julian
oscillation).
- The seasonal/annual cycle, forced by the annual cycle of the sun
due to the rotation of the Earth about the sun.
- Interannual or year-to-year variability, such as El Nino.
Accurate forecasting of this variability will benefit people living
in the tropical regions, and also over the rest of the Earth due to
remote 'teleconnections' between the weather in the tropics and the
weather elsewhere around the globe. Here, we focus on variability on
the intraseasonal time scale, which is dominated by the Madden-Julian
oscillation (MJO). This was discovered by Madden and Julian (1971,
1972)
who called it the '40-50-day oscillation' because of its preferred
time scale. Since then it has been called the '30-60-day oscillation'
and the 'intraseasonal oscillation', but the term 'MJO' has now
emerged as a favourite. Zhang (2005) gives a
comprehensive review of the MJO. Further information can be found in
research papers on the MJO.
MJO rainfall cycle
The MJO is characterised by an eastward propagation of
rainfall over the 'warm pool' region from the Indian Ocean to the
western Pacific. A composite mean MJO cycle, calculated by averaging
over many individual MJO events, is shown in the animation in Fig. 1.
The animation is on an endless loop, showing maps every day through
the MJO cycle, which is given a nominal period here of 48 days. The
maps show 'anomalies' of rainfall, where the climatological mean
rainfall map has been subtracted to leave the remainder. Where this
remainder, or anomaly, is positive (blue shading), there is more
rainfall than usual, i.e., conditions are wetter than normal. Where
it is negative (red shading), there is less rainfall than usual, and
conditions are drier than normal.
The MJO cycle presented here is as defined by Wheeler
and Hendon (2004). Back in the day, there was no universally
accepted definition of the MJO, and each MJO paper spent its first
section defining its own, slightly different definition of the MJO,
which made comparison of results rather difficult. However, the
real-time multivariate (RMM) MJO index of Wheeler and Hendon is now
the accepted standard definition. So thanks guys. The MJO cycle, as
defined by the RMM index, is split up into 8 phases, for convenience.
Each phase corresponds to 1/8 of the full cycle. An individual MJO
event can last anywhere between 30 and 60 days (the MJO is a
'broadband' phenomenon). In the animation here, a typical period of
48 days is assumed, which conveniently means that each of the 8
RMM phases lasts 48/8=6 days. At the top right of Figure 1, an 'MJO
clock' sweeps round showing the current RMM phase, and the actual
values of the current RMM phase and day are shown to the left.
An MJO cycle can be considered to start in RMM phase 1. In phase
1, enhanced rainfall (blue shading) develops over the western Indian
Ocean. This region of enhanced rainfall (wet conditions) then moves
slowly eastward over the Indian Ocean in phase 2 and 3. By phase 4,
it has reached the 'maritime continent' (the archipelago of islands
and shallow seas of Indonesia and surrounding countries). In phase 6,
7 and 8, it propagates further eastward over the western Pacific,
eventually dying out in the central Pacific. The next MJO cycle then
starts, with enhanced rainfall over the western Indian Ocean in phase
1. Behind the region of enhanced rainfall is a region of suppressed
rainfall (negative anomalies, red shading). Hence, at certain times
during the MJO cycle there is a 'dipole' of rainfall anomalies. For
example, in phase 6 there is enhanced rainfall over the western
Pacific and suppressed rainfall over the Indian Ocean. In phase 2,
the dipole is in the opposite sense (dry over the western Pacific and
wet over the Indian Ocean).
Figure 1. MJO cycle of
precipitation anomalies (CMAP data set). The life cycle is calculated
from MJO events in the November-April (northern hemisphere winter)
season only. Composite maps were calculated for each of the 8 RMM
phases, and linearly interpolated for the intermediate days to give a
smooth cycle. In addition to the colour shading, a thick solid
contour at 1 mm day-1 outlines the region of enhanced
rainfall, and a thick dashed contour at -1 mm day-1
outlines the region of suppressed rainfall. These contours are
reproduced in subsequent animations below to indicate the main regions
of MJO precipitation.
|
The rainfall anomalies shown above are calculated using the CMAP
precipitation data set. This is a global data set, mainly based on
satellite measurements of cloud top temperatures, and calibrated
against ground based rain gauges. Although the data set has a
relatively low horizontal resolution (grid length of 2.5 degrees, or
approximately 250 km), its length (it begins in 1979) makes it very
useful for defining the coarse-grained features of the MJO, as in
Figure 1.
An alternative rainfall data set is available from the TRMM
(Tropical Rainfall Measuring Mission) satellite. This has a much
higher horizontal resolution (0.25 degrees, approximately 25 km). An
animation of MJO rainfall anomalies from the TRMM data set is shown in
Figure 2. The basic structure of the TRMM rainfall anomalies in
Figure 2 agrees with the CMAP rainfall anomalies in Figure 1.
However, much finer scale behaviour can be seen in the TRMM data set,
especially around the islands of the maritime continent.
Figure 2. MJO cycle of
precipitation anomalies (TRMM data set). The thick solid and dashed
contours outline the main regions of enhanced and suppressed MJO
rainfall from the CMAP data set, respectively, as in Figure
1.
|
Further reading:
Matthews AJ, Pickup G, Peatman SC, Clews P, Martin J,
2013:
The effect of the Madden-Julian Oscillation on station rainfall and river level in the Fly River system, Papua New Guinea.
J. Geophys. Res., 118, 10926-10935.
| Abstract
|
|
Matthews AJ, Li HYY,
2005:
Modulation of station rainfall over the western Pacific by the Madden-Julian Oscillation.
Geophys. Res. Lett., 32, L14827, doi: 10.1029/2005GL023595.
| Abstract
|
|
MJO tropical dynamics
In addition to strongly modulating the rainfall in the tropics, the
MJO has a signal in other meteorological variables. For example, a
clear MJO cycle in sea level pressure can also be seen (Figure 3).
The negative sea level pressure anomalies (where the pressure is lower
than usual) are coloured blue, and the positive sea level pressure
anomalies (where the pressure is higher than usual) are coloured red
(see the legend for exact values). The sea level pressure signal is
clearly related to the cycle in rainfall; the thick solid contour
outlines the area of enhanced rainfall that was shown in detail in
Fig. 1, and the thick dashed contour outlines the area of suppressed
rainfall.
The negative pressure anomalies appear to emanate out of the region
of enhanced rainfall. One signal propagates eastward along the
equator. This is an equatorial Kelvin wave. When it reaches the
Andes mountain range along the eastern coast of the Pacific it is
momentarily blocked, before continuing on eastward across the
Atlantic, completing a circuit of the equator in one MJO cycle, about
48 days.
An equatorial Rossby wave signal is also forced by the MJO rainfall
anomalies. This can be seen as a pair of negative sea level pressure
anomalies, one either side of the equator, that lie slightly to the
west of the enhanced rainfall.
In the 'other half' of the MJO cycle, the reduced rainfall triggers
equatorial Kelvin and Rossby waves of the opposite sign (positive sea
level pressure anomalies).
Figure 3. MJO cycle of sea
level pressure anomalies (NCEP-DOE reanalysis data set). The thick
solid and dashed contours outline the main regions of enhanced and
suppressed MJO rainfall, respectively, as in Figure
1.
|
These equatorial Kelvin and Rossby waves are well understood
theoretically through the Matsuno-Gill model of tropical dynamics
(Matsuno, 1966; Gill, 1980). In
the many individual cumulonimbus and other clouds that produce rain in
the tropics, water vapour condenses to form liquid water or ice, that
eventually falls out as rain. This condensation releases (latent)
heat into the atmosphere, and this heat source can then drive the
tropical circulation. An animation of the equatorial Kelvin-Rossby
wave response to a heat source on the equator in a numerical model of
the atmosphere (Figure 4) simulates many of the aspects of the
observed MJO sea level pressure cycle in Figure 3.
Figure 4. Equatorial
Kelvin-Rossby wave response to fixed equatorial heating in a shallow
water model of the atmosphere. The upper panel represents the
pressure and wind distribution in the upper atmosphere. The middle
panel represents the vertical and zonal circulation in an equatorial
plane. The lower panel represents the pressure and wind distribution
in the lowerr atmosphere.
|
The MJO also affects other meteorological systems in the tropics:
- Monsoons. The MJO modulates the active/break cycles that occur
within the Asian and West African monsoons.
- Tropical cyclones/hurricanes. The MJO modulates tropical
cyclone numbers.
- El Nino - Southern Oscillation (ENSO). The MJO has been instrumental in triggering recent El Nino episodes.
- South Pacific Convergence Zone (SPCZ). The MJO affects the route taken by extratropical cyclones that propagate into the tropics and trigger convection along the SPCZ.
Further reading:
Birch CE, Webster S, Peatman SC, Parker DJ, Matthews AJ, Li Y, Hassim ME,
2016:
Scale interactions between the MJO and the western Maritime Continent.
J. Climate, 29, 2471-2492.
| Abstract
|
|
Peatman SC, Matthews AJ, Stevens DP,
2015:
Propagation of the Madden-Julian Oscillation and scale interaction with the diurnal cycle in a high-resolution GCM.
Climate Dyn., 45, 2901-2918.
| Abstract
|
|
Peatman SC, Matthews AJ, Stevens DP,
2014:
Propagation of the Madden-Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation.
Quart. J. Roy. Meteorol. Soc., 140, 814-825.
| Abstract
|
|
Matthews AJ,
2012:
A multiscale framework for the origin and variability of the South Pacific Convergence Zone.
Quart. J. Roy. Meteorol. Soc., 138, 1165-1178.
| Abstract
|
|
Lavender SL, Taylor CM, Matthews AJ,
2010:
Coupled land-atmosphere intraseasonal variability of the West African monsoon in a GCM.
J. Climate, 23, 5557-5571.
| Abstract
|
|
Love BS, Matthews AJ,
2009:
Real-time localised forecasting of the Madden-Julian Oscillation using neural network models.
Quart. J. Roy. Meteorol. Soc., 135, 1471-1483.
| Abstract
|
|
Lavender SL, Matthews AJ,
2009:
Response of the West African monsoon to the Madden-Julian Oscillation.
J. Climate, 22, 4097-4116.
| Abstract
|
|
Love BS, Matthews AJ, Janacek GJ,
2008:
Real-time extraction of the Madden-Julian Oscillation using empirical mode decomposition and statistical forecasting with a VARMA model.
J. Climate, 21, 5318-5335.
| Abstract
|
|
Matthews AJ,
2008:
Primary and successive events in the Madden-Julian Oscillation.
Quart. J. Roy. Meteorol. Soc., 134, 439-453.
| Abstract
|
|
Pohl B, Matthews AJ,
2007:
Observed changes in the lifetime and amplitude of the Madden-Julian Oscillation associated with interannual ENSO sea surface temperature anomalies.
J. Climate, 20, 2659-2674.
| Abstract
|
|
Matthews AJ,
2004:
Intraseasonal variability over tropical Africa during northern summer.
J. Climate, 17, 2427-2440.
| Abstract
|
|
Hall JD, Matthews AJ, Karoly DJ,
2001:
The modulation of tropical cyclone activity in the Australian region by the Madden-Julian Oscillation.
Mon. Wea. Rev., 129, 2970-2982.
| Abstract
|
|
Matthews AJ,
2000:
Propagation mechanisms for the Madden-Julian Oscillation.
Quart. J. Roy. Meteorol. Soc., 126, 2637-2652.
| Abstract
|
|
Matthews AJ, Kiladis GN,
2000:
A model of Rossby waves linked to submonthly convection over the eastern tropical Pacific.
J. Atmos. Sci., 57, 3785-3798.
| Abstract
|
|
Matthews AJ, Kiladis GN,
1999:
Interactions between ENSO, transient circulation and tropical convection over the Pacific.
J. Climate, 12, 3062-3086.
| Abstract
|
|
Matthews AJ, Kiladis GN,
1999:
The tropical-extratropical interaction between high-frequency transients and the Madden-Julian Oscillation.
Mon. Wea. Rev., 127, 661-677.
| Abstract
|
|
Matthews AJ, Slingo JM, Hoskins BJ, Inness PM,
1999:
Fast and slow Kelvin waves in the Madden-Julian Oscillation of a GCM.
Quart. J. Roy. Meteorol. Soc., 125, 1473-1498.
| Abstract
|
|
Matthews AJ, Lander J,
1999:
Physical and numerical contributions to the structure of Kelvin wave-CISK modes in a spectral transform model.
J. Atmos. Sci., 56, 4050-4058.
| Abstract
|
|
Slingo JM, Sperber KR, Boyle JS, Ceron JP, Dix M, Dugas B, Ebisuzaki W, Fyfe J, Gregory D, Gueremy JF, Hack J, Harzallah A, Inness P, Kitoh A, Lau WKM, McAvaney B, Madden R, Matthews AJ, Palmer TN, Park CK, Randall D, Renno N,
1996:
Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject.
Climate Dyn., 12, 325-358.
| Abstract
|
|
Matthews AJ, Hoskins BJ, Slingo JM, Blackburn M,
1996:
Development of convection along the SPCZ within a Madden-Julian Oscillation.
Quart. J. Roy. Meteorol. Soc., 122, 669-688.
| Abstract
|
|
Global structure of the MJO
The MJO is not just confined to the tropics. Figure 5 shows an
animation of the MJO cycle in 200-hPa streamfunction anomalies.
Streamfunction is a measure of the circulation in the atmosphere. The
wind blows parallel to streamfunction contours: clockwise around
positive streamfunction anomalies (red shading), and anticlockwise
around negative streamfunction anomalies (blue shading). 200 hPa is a
pressure level in the upper troposphere, about 8 km above the Earth's
surface. It is the level at which the jet streams are strongest, and
the MJO streamfunction anomalies in Figure 5 represent changes to the
position and strength of the jet streams. The animation in Figure 5
shows that the MJO signal is truly global, with planetary-scale Rossby
wave trains reaching out from the tropical core region out into the
extratopics and high latitudes.
Figure 5. MJO cycle of 200-hPa streamfunction anomalies (NCEP-DOE reanalysis data set).
|
Further reading:
Ocean component of the MJO
The MJO is not just confined to the atmosphere. It has a strong
signal in the ocean, with warm sea surface temperatures (SSTs) leading
the enhanced rainfall, and cool SSTs leading the reduced rainfall
(Figure 6). This has lead to theories of the MJO based on coupled
ocean-atmosphere interactions.
Figure 6. MJO cycle of sea
surface temperature anomalies (NOAA OI v2 data set).
|
Further reading:
Baranowski DB, Flatau MK, Flatau PJ, Matthews AJ,
2016:
Impact of atmospheric convectively-coupled Kelvin waves on upper ocean variability.
J. Geophys. Res., 121, 2045-2059.
| Abstract
|
|
Matthews AJ, Baranowski DB, Heywood KJ, Flatau PJ, Schmidtko S,
2014:
The surface diurnal warm layer in the Indian Ocean during CINDY/DYNAMO.
J. Climate, 27, 9101-9122.
| Abstract
|
|
Webber BGM, Matthews AJ, Heywood KJ, Kaiser J, Schmidtko S,
2014:
Seaglider observations of equatorial Indian Ocean Rossby waves associated with the Madden-Julian Oscillation.
J. Geophys. Res., 119, 3714-3731.
| Abstract
|
|
Webber BGM, Stevens DP, Matthews AJ, Heywood KJ,
2012:
Dynamical ocean forcing of the Madden-Julian Oscillation at lead times of up to five months.
J. Climate, 25, 2824-2842.
| Abstract
|
|
Webber BGM, Matthews AJ, Heywood KJ, Stevens DP,
2012:
Ocean Rossby waves as a triggering mechanism for primary Madden-Julian events.
Quart. J. Roy. Meteorol. Soc., 138, 514-527.
| Abstract
|
|
Matthews AJ, Singhruck P, Heywood KJ,
2010:
Ocean temperature and salinity components of the Madden-Julian Oscillation observed by Argo floats.
Climate Dyn., 35, 1149-1168.
| Abstract
|
|
Webber BGM, Matthews AJ, Heywood KJ,
2010:
A dynamical ocean feedback mechanism for the Madden-Julian Oscillation.
Quart. J. Roy. Meteorol. Soc., 136, 740-754.
| Abstract
|
|
Matthews AJ, Singhruck P, Heywood KJ,
2007:
Deep ocean impact of a Madden-Julian Oscillation observed by Argo floats.
Science, 318, 1765-1769.
| Abstract
|
|
Batstone CP, Matthews AJ, Stevens DP,
2005:
Coupled ocean-atmosphere interactions between the Madden-Julian Oscillation and synoptic-scale variability over the warm pool.
J. Climate, 18, 2004-2020.
| Abstract
|
|
Matthews AJ, Meredith MP,
2004:
Variability of Antarctic circumpolar transport and the southern annular mode associated with the Madden-Julian Oscillation.
Geophys. Res. Lett., 31, L24312, doi: 10.1029/2004GL021666.
| Abstract
|
|
Matthews AJ,
2004:
The atmospheric response to observed intraseasonal tropical sea surface temperature anomalies.
Geophys. Res. Lett., 31, L14107, doi: 10.1029/2004GL020474.
| Abstract
|
|
|