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  • View in gallery

    (above). Phase diagram of the RMM index. Each point represents a day. Eight phases and corresponding approximate locations of enhanced convective signals of the MJO are labeled. Points within the circle represent weak or no MJO (from Wheeler and Hendon 2004).

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    (right). Composites of intraseasonal (30– 90 days) anomalies in TRMM precipitation (mm day−1) during November–April of 1998–2012 based on the RMM index.

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    Rainfall anomalies measured by surface rain gauges during MJO phases (a) 2, (b) 4, (c) 6, and (d) 8 for austral winter. Anomalies are expressed as maximum vertical distance between the unconditional cumulative distribution function (CDF) and the corresponding conditional CDF for a particular MJO phase (vertical differences are measured at the point of maximum divergence in dimensionless units of “percent change in probability”). Positive (negative) distances indicate evidence of enhanced (suppressed) rainfall during the respective phase (from Donald et al. 2006).

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    TC tracks (1975–2011) and precipitation anomalies (1998–2011) in MJO phases 2 and 3, 4 and 5, 6 and 7, and 8 and 1 when the amplitude of the RMM index is greater than one. The total number of days of TCs in each phase group is listed. The TC tracks are from the International Best Track Archive for Climate Stewardship (IBTrACS) v03r04. Precipitation data are from Tropical Rainfall Measuring Mission (TRMM) 3B43 v7.

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    Locations of large floods during 1985–2010 based on the Dartmouth Flood Observatory Global Archive of Large Flood Events at University of Colorado (http://floodobservatory.colorado.edu/Archives/index.html). Red boxes mark regions where probability of total flood days and/or events are significantly affected by the MJO. Blue and green boxes are examples shown in Figs. 4 and 5, respectively.

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    MJO influences of large floods of the West Coast of North America (blue box in Fig. 3). (a) Monthly probability of total flood days during MJO episodes (black bars) and when there is no MJO (gray). (b) Probability of flood events in each MJO phase (total number of flood events starting in each phase divided by the total number of days in that phase). Vertical error bars denote ranges of the 95% confidence level.

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    MJO influences of large floods of the Philippines (green box in Fig. 3). (a) Monthly probability of total flood days during MJO episodes (black bars) and when there is no MJO (gray). (b) Probability of total flood days in each MJO phase (total number of flood days in each phase divided by the total number of days in that phase). (c) Probability of flood events as functions of MJO phases. Vertical error bars denote ranges of the 95% confidence level.

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    Ratio of fire detections for MJO phases 5–8 over 1–4 in June–November (from Reid et al. 2012).

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    Total fire carbon emission (g C m2 day−1) based on data of Mu et al. (2011). Red boxes indicate regions where fire emission fluctuates substantially with MJO phases.

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    Summer (June–September) lightning frequency rank-order anomalies (z scores) stratified by RMM phases (denoted in lower-right corner). Red (blue) shading denotes areas of enhanced (suppressed) lighting activity for each RMM phase exceeding the 95% confidence interval (from Abatzoglou and Brown 2009).

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    December–February composites of surface air temperature anomalies (°C) for each MJO phase (from Zhou et al. 2012).

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Madden–Julian Oscillation: Bridging Weather and Climate

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  • 1 RSMAS, University of Miami, Miami, Florida
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The Madden–Julian oscillation exerts broad influences on global weather and climate as its center of convection moves from the tropical Indian Ocean into the Pacific. Weather events under the influence of the MJO include precipitation, surface temperature, tropical cyclones, tornadoes, flood, wildfire, and lightning, among others. Several climate phenomena are also affected by the MJO. They are the monsoons, El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific and North American pattern, the Arctic and Antarctic Oscillations or northern and southern annual modes, the Indian Ocean dipole, the Wyrtki jets, and the Indonesian Through-flow. This article provides a brief summary of the connections between the MJO and these weather and climate phenomena. These connections demonstrate the critical role of the MJO in the weather–climate continuum and its prediction.

CORRESPONDING AUTHOR: Chidong Zhang, RSMAS/MPO, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33133, E-mail: czhang@rsmas.miami.edu

The Madden–Julian oscillation exerts broad influences on global weather and climate as its center of convection moves from the tropical Indian Ocean into the Pacific. Weather events under the influence of the MJO include precipitation, surface temperature, tropical cyclones, tornadoes, flood, wildfire, and lightning, among others. Several climate phenomena are also affected by the MJO. They are the monsoons, El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific and North American pattern, the Arctic and Antarctic Oscillations or northern and southern annual modes, the Indian Ocean dipole, the Wyrtki jets, and the Indonesian Through-flow. This article provides a brief summary of the connections between the MJO and these weather and climate phenomena. These connections demonstrate the critical role of the MJO in the weather–climate continuum and its prediction.

CORRESPONDING AUTHOR: Chidong Zhang, RSMAS/MPO, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33133, E-mail: czhang@rsmas.miami.edu

As a prime example of intraseasonal variability, the Madden–Julian Oscillation affects— and is pivotal to predicting—both weather and climate.

The conceptual separation of weather and climate is deeply rooted in our daily experience, as Herbertson (1901) put it: “Climate is what on an average we may expect, weather is what actually we get.”1 Translated into a scientific language, weather is a state of the atmosphere at a particular instance and climate is a set of statistics of an ensemble of many different states (Lorenz 1975). The weather–climate separation had its scientific basis in numerical prediction. It has been perceived that weather predictability comes from initiation conditions, while climate predictability from boundary conditions (Charney and Shukla 1977). This distinction would cease to exist in the modern practice of “seamless prediction” for weather and climate using “unified prediction models” (Hurrell et al. 2009; Brown et al. 2012). In such models, all components of the Earth system are coupled to each other, the only boundary condition needed is at the top of the atmosphere, and the source of predictability comes from, in addition to initiation conditions, the “memory” of slowly varying subsystems (the ocean, soil moisture, land, and sea ice), quasiperiodic phenomena, and known external forcing (Lorenz 1975). Yet, the weather–climate separation has penetrated so deep in our thinking that their traditional definitions are still often used in scientific and official documents, leaving a gaping vacancy in between. This vacancy is occupied by intraseasonal (20–90 days) variability.

Intraseasonal variability is by no means merely red noise filling the gap between synoptic and seasonal variability. Intraseasonal phenomena are distinct from higher- and lower-frequency variability by their significant spectral peaks and coherent spatial patterns. The Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972) is the best example. Its large-scale signals in the atmospheric circulation, deep convection, and other variables propagating eastward slowly (~5 m s−1) from the Indian to Pacific Oceans are the dominant component of the tropical intraseasonal variability. They are so robust that they can be discerned from raw data without statistical manipulation (Zhang 2005).

The MJO plays a critical role in connecting or bridging weather and climate. This bridging role can be appreciated from different perspectives. The MJO affects many weather and climate phenomena. Its effects on weather depend on the state or phase of certain climate phenomena (e.g., ENSO), and their combined effects may lead to extreme weather events. Climate modes under the influence of the MJO in turn modulate weather in many parts of the world. The MJO is involved in scale interactions across a wide range of spectrum from the diurnal cycle to interannual variability (Moncrieff et al. 2012). Forecast of the Earth system to serve the society requires seamless prediction that covers daily, intraseasonal, seasonal, interannual, and longer variabilities (Dole 2008; Brunet et al. 2010; Shapiro et al. 2010; Chang et al. 2011). Improved MJO forecasting benefits prediction of tropical cyclones (Vitart 2009; Vitart et al. 2010), extratropical weather regimes (Marshall et al. 2010; Vitart and Molteni 2010), and ENSO (Shi et al. 2009); serves users from many sectors of the society (Gottschalck et al. 2010); and helps close the gap between traditional weather and short-term climate prediction (Waliser et al. 2006).

This article provides a brief summary of MJO effects on certain types of weather and climate events. The author hopes to convince its readers that weather and climate must be treated as a continuum by including the MJO and intraseasonal variability in general and reinforce the notion that the societal need for weather and climate prediction must be met with improved understanding and forecast of the MJO (Waliser et al. 2003a).

PRECIPITATION.

The sidebar “Detecting MJO influences on weather and climate” (Fig. SB2) illustrates rainfall variability in the tropics associated with the MJO during boreal winter (November–April). MJO influences on precipitation are not limited to the tropics and this season. A global map of precipitation anomalies associated with MJO in austral winter is given in Fig. 1. Anomalies in precipitation change signs between MJO phases in many places of the world.

DETECTING MJO INFLUENCES ON WEATHER AND CLIMATE

When discussing possible effects of the MJO on a particular type of weather or climate events, we must be mindful that all MJO episodes do not cause those events and all those events are not related to the MJO. The issue is whether and how the MJO may modulate the chances of occurrence, strengths, or spatial patterns/distributions of those events, as illustrated by examples given in this article.

MJO influences on weather events are commonly described as how those events vary with its phases. MJO phases can be defined in terms of the timing and locations of its center of convection (maximum rainfall anomalies) and associated wind fields. Most commonly used MJO phases are based on the real-time multivariate MJO (RMM) index of Wheeler and Hendon (2004). The RMM index is derived from a combined EOF analysis of daily anomalies in upper-and lower-level zonal wind and outgoing longwave radiation (OLR). MJO phases are defined by the principle components of the first two leading EOFs, normalized by their standard deviation (Fig. SB1). Each day, represented by a dot on the phase diagram, belongs to a particular phase. The distance of the dot from the center measures the amplitude of the MJO on that day. Composites of rainfall or any other field for each phase illustrate the canonical behavior of the MJO. In the boreal winter composite (Fig. SB2), the convection center of the MJO, represented by the maximum of positive anomalies in rainfall, starts over the Indian Ocean in phases 1–3, passes through the Maritime Continent in phases 4 and 5 and into the western Pacific in phases 6 and 7, and may continue their circumnavigating journey into the western hemisphere in phases 8 and 1 and thus complete its full cycle. During boreal summer, the zonal movement of the MJO convection center is accompanied by an additional northward movement associated with the Asian summer monsoon (Lawrence and Webster 2002). When the amplitude is less than 1 (within the circle on the phase diagram), the MJO is considered very weak or not existing (no MJO) and can be assigned as phase 0.

Fig. SB1
Fig. SB1

(above). Phase diagram of the RMM index. Each point represents a day. Eight phases and corresponding approximate locations of enhanced convective signals of the MJO are labeled. Points within the circle represent weak or no MJO (from Wheeler and Hendon 2004).

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

Fig. SB2
Fig. SB2

(right). Composites of intraseasonal (30– 90 days) anomalies in TRMM precipitation (mm day−1) during November–April of 1998–2012 based on the RMM index.

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

MJO influences on climate may also depend on its phases. Some climate events are more likely to start, amplify, or change sign in certain MJO phases than others. Some other climate phenomena are related to activities of a group of MJO events over a period (e.g., a season), instead of phases of individual MJO events. There can be a time lag between the group MJO activities and the climate phenomena they affect.

Fig. 1.
Fig. 1.

Rainfall anomalies measured by surface rain gauges during MJO phases (a) 2, (b) 4, (c) 6, and (d) 8 for austral winter. Anomalies are expressed as maximum vertical distance between the unconditional cumulative distribution function (CDF) and the corresponding conditional CDF for a particular MJO phase (vertical differences are measured at the point of maximum divergence in dimensionless units of “percent change in probability”). Positive (negative) distances indicate evidence of enhanced (suppressed) rainfall during the respective phase (from Donald et al. 2006).

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

All monsoon systems undergo intraseasonal fluctuations (Lau and Waliser 2012). The MJO is a prominent source of the monsoon intraseasonal fluctuations. It affects the Asian summer monsoon mainly through, in addition to its eastward propagation, its northward propagation, which is unique in boreal summer. The onset of the South Asian monsoon is more likely to occur when MJO convection just starts over the Indian Ocean or in MJO phases 2 and 3 than in other phases. There are typically three or four major northward-propagating MJO events during a monsoon season, each inducing a local intraseasonal spike in rainfall. About 50%–80% of the total intraseasonal variance in the Asian summer monsoon rainfall is related to the MJO. On top of that, rainfall from synoptic monsoon lows and depressions enhanced by the MJO increases the chance of floods. Goswami (2012) and Hsu (2012) provided detailed descriptions of the role of the MJO in the Asian summer monsoon.

The MJO affects the Australian monsoon as its convection center propagates eastward, passing over the northern part of Australia (Wheeler et al. 2009). MJO accounts for more than 80% of onset dates of the Australian monsoon. Heavy rain (weekly rainfall in the top quintile of the December–February season) varies from 130 mm near the coast to 10 mm in central Australia. The probability of heavy rain at a given location depends on the longitudes of the MJO convection center. A detailed review on the role of the MJO in the Australia monsoon is given by Wheeler and McBride (2012).

Large-scale perturbations that are excited by MJO convection and propagate into the Americas can induce intra-seasonal fluctuations in rainfall of the American monsoons. In austral summer, rainfall over southern Brazil is heavier than normal (by up to 15–20 mm day−1; 50%–75% of the mean) when MJO convection moves into the central Pacific, especially east of the date line, or when it starts over the Indian Ocean, but it is lighter than normal when the MJO convection center is near or immediately east of the Maritime Continent. In boreal summer, MJO convective activities along the ITCZ over the northeastern tropical Pacific make it easier for the MJO to influence the North American monsoon. Its rainfall can differ as much as 25%–100% at individual stations between opposite phases of the MJO during July–September. The largest changes are along the Pacific coast, over southern Mexico and Central America, and on the Gulf coast of Mexico. Mo et al. (2012) provided a detailed summary of the role of the MJO in the pan-American monsoons.

Over West Africa, intraseasonal variability accounts about 30% of the total monsoon rainfall. One-third of the intraseasonal variability is related to the MJO or the African MJO mode (Janicot et al. 2011). At certain locations rainfall fluctuates by a factor of 2 between MJO phases. Near Lake Chad, about 50% of the amplitude of intraseasonal convective anomalies is related to the MJO (Alaka and Maloney 2012). From there, rainfall anomalies move westward to the rest of West Africa, presumably related to the Rossby waves generated by MJO convection over the Indian Ocean (Matthews 2004). As these convective systems move westward, some of them become part of African easterly waves. These African easterly waves are enhanced when the MJO convection center is over the Indian Ocean and suppressed when it is over the Maritime Continent and western Pacific (Ventrice et al. 2011). Janicot et al. (2011) and Barlow (2012) described in detail the MJO influences on the West African monsoon.

MJO also influences precipitation outside the monsoon regions and monsoon rainy seasons. Examples can be found in the Middle East and Southwest Asia (Barlow et al. 2005; Barlow 2012), Southeast and East Asia (Jeong et al. 2008; Zhang et al. 2009; He et al. 2011; Jia et at. 2011), equatorial Africa (Pohl and Camberlin 2006a, b; Laing et al. 2011), Brazil (Carvalho et al. 2004; Souza and Ambrizzi 2006), Chile (Barrett et al. 2012), and North America (Bond and Vecchi 2003; Becker et al. 2011).

MJO influences on precipitation extend to extreme rainfall, defined as precipitation breaking the records or within a given top percentile. On a global scale, extreme rainfall events during active MJO periods (phases 1–8) are about 40% higher than in its quiescent periods (phase 0) (Jones et al. 2004). The MJO might have been one of the factors for the heavy snowfalls in the Tokyo metropolitan area on 3 February 2008 (Yamakawa and Suppiah 2009). The record-breaking snow events in the eastern United States in December 2009 and February 2010 was attributed to the MJO with its unusual active convection over the central Pacific during an El Niño year on top of the negative phase of the North Atlantic Oscillation (Moon et al. 2012). Over the highland region of east equatorial Africa, 62% of extreme rainfall events in March–May occur when MJO convection over the Indian Ocean is active, while 72% of extreme rainfall near the coastal region occurs when MJO convection is suppressed over the Indian Ocean and Maritime Continent. There, negative anomalies during weak MJO years often follow the peaks of ENSO warm events (Pohl and Camberlin 2006b). In the subtropical, semiarid north-central coastal area of Chile (30°S), about 80% of the strong precipitation events (normally 3–5 per year) during the fall and winter of rainy years are related to enhanced MJO convection in the central equatorial Pacific (Juliá et al. 2012).

Extreme rainfall (exceeding 90th percentile of frequency distribution in intensity and spatial coverage) in boreal winter occurs twice more frequently over the contiguous United States when the MJO is active (phases 1–8) than inactive (phase 0) and most frequently when the MJO convection center is over the Indian Ocean (Jones and Carvalho 2012). Such MJO influences are stronger during ENSO warm than cold events. Forecast skill for the winter extreme precipitation is higher when MJO convection over the Indian–western Pacific Oceans are suppressed (Jones et al. 2011a,b).

The MJO affects precipitation in remote areas by modifying the strength of meridional overturning circulations (Zhang et al. 2009; He et al. 2011) and moisture transport (Jeong et al. 2008; Jia et al. 2011), exciting Rossby wave trains that emanate from the tropics into the extratropics (Grimm and Silva Dias 1995), and forcing zonally propagating equatorial Rossby and Kelvin waves (Matthews 2004; Janicot et al. 2009). A phenomenon known as the “atmospheric river” acts as a conveyer belt to transport moisture from the tropical central Pacific to the West Coast of the United States, where it may cause torrential rain and floods (Dettinger 2011). MJO may enhance rainfall along the West Coast of the United States through strengthening the atmospheric river (Ralph et al. 2011). As a result, total snow accumulation in the Sierra Nevada significantly increases (reduces) when MJO convection is active over the eastern Indian Ocean (Western Hemisphere); the corresponding magnitude of daily anomalies is about half the daily mean in the cold season (Guan et al. 2012).

TORNADOES.

Violent tornado outbreak days, with six or more tornadoes on the (Enhanced) Fujita [(E)F] scale of at least (E)F2 magnitude reported within a 24-h period, tend to occur in spring over the contiguous United States. Thompson and Roundy (2013) documented that violent tornado outbreak days during March, April, and May are more than twice as frequent during MJO phase 2 as during other phases, including phase 0. Atmospheric conditions favorable for tornado formation can be provided by combined intraseasonal and seasonal anomalous patterns in upper-tropospheric troughs and upper- and lower-tropospheric winds.

TROPICAL CYCLONES.

Favorable large-scale conditions for genesis, intensification, and longevity of tropical cyclones (TCs) can be altered by the MJO. Figure 2 shows TC tracks in different MJO phases and composites of precipitation anomalies of the MJO. The density of the tracks indicates total TC days or the TC occurrence frequency when normalized by the total days of each MJO phase. This figure summarizes the known MJO modulation of TCs that has been documented by many studies—some of which are mentioned below. It shows an obvious eastward shift of the most dense TC tracks along with the positive precipitation anomalies of the MJO from the Indian Ocean to the eastern Pacific.