Modulation of Daily Precipitation over Southwest Asia by the Madden–Julian Oscillation

Mathew Barlow Atmospheric and Environmental Research, Inc., Lexington, Massachusetts

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Matthew Wheeler Bureau of Meteorology Research Centre, Melbourne, Australia

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Bradfield Lyon International Research Institute for Climate Prediction, Palisades, New York

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Heidi Cullen The Weather Channel, and Georgia Institute of Technology, Atlanta, Georgia

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Abstract

Analysis of daily observations shows that wintertime (November–April) precipitation over Southwest Asia is modulated by Madden–Julian oscillation (MJO) activity in the eastern Indian Ocean, with strength comparable to the interannual variability. Daily outgoing longwave radiation (OLR) for 1979–2001 is used to provide a long and consistent, but indirect, estimate of precipitation, and daily records from 13 stations in Afghanistan reporting at least 50% of the time for 1979–85 are used to provide direct, but shorter and irregularly reported, precipitation data. In the station data, for the average of all available stations, there is a 23% increase in daily precipitation relative to the mean when the phase of the MJO is negative (suppressed tropical convection in the eastern Indian Ocean), and a corresponding decrease when the MJO is positive. The distribution of extremes is also affected such that the 10 wettest days all occur during the negative MJO phase. The longer record of OLR data indicates that the effect of the MJO is quite consistent from year to year, with the anomalies averaged over Southwest Asia more negative (indicating more rain) for the negative phase of the MJO for each of the 22 yr in the record. Additionally, in 9 of the 22 yr the average influence of the MJO is larger than the interannual variability (e.g., the relationship results in anomalously wet periods even in dry years and vice versa).

Examination of NCEP–NCAR reanalysis data shows that the MJO modifies both the local jet structure and, through changes to the thermodynamic balance, the vertical motion field over Southwest Asia, consistent with the observed modulation of the associated synoptic precipitation. A simple persistence scheme for forecasting the sign of the MJO suggests that the modulation of Southwest Asia precipitation may be predictable for 3-week periods. Finally, analysis of changes in storm evolution in Southwest Asia due to the influence of the MJO shows a large difference in strength as the storms move over Afghanistan, with apparent relevance for the flooding event of 12–13 April 2002.

Corresponding author address: Dr. Mathew Barlow, University of Massachusetts—Lowell, One University Avenue, Lowell, MA 01854. Email: Mathew_Barlow@uml.edu

Abstract

Analysis of daily observations shows that wintertime (November–April) precipitation over Southwest Asia is modulated by Madden–Julian oscillation (MJO) activity in the eastern Indian Ocean, with strength comparable to the interannual variability. Daily outgoing longwave radiation (OLR) for 1979–2001 is used to provide a long and consistent, but indirect, estimate of precipitation, and daily records from 13 stations in Afghanistan reporting at least 50% of the time for 1979–85 are used to provide direct, but shorter and irregularly reported, precipitation data. In the station data, for the average of all available stations, there is a 23% increase in daily precipitation relative to the mean when the phase of the MJO is negative (suppressed tropical convection in the eastern Indian Ocean), and a corresponding decrease when the MJO is positive. The distribution of extremes is also affected such that the 10 wettest days all occur during the negative MJO phase. The longer record of OLR data indicates that the effect of the MJO is quite consistent from year to year, with the anomalies averaged over Southwest Asia more negative (indicating more rain) for the negative phase of the MJO for each of the 22 yr in the record. Additionally, in 9 of the 22 yr the average influence of the MJO is larger than the interannual variability (e.g., the relationship results in anomalously wet periods even in dry years and vice versa).

Examination of NCEP–NCAR reanalysis data shows that the MJO modifies both the local jet structure and, through changes to the thermodynamic balance, the vertical motion field over Southwest Asia, consistent with the observed modulation of the associated synoptic precipitation. A simple persistence scheme for forecasting the sign of the MJO suggests that the modulation of Southwest Asia precipitation may be predictable for 3-week periods. Finally, analysis of changes in storm evolution in Southwest Asia due to the influence of the MJO shows a large difference in strength as the storms move over Afghanistan, with apparent relevance for the flooding event of 12–13 April 2002.

Corresponding author address: Dr. Mathew Barlow, University of Massachusetts—Lowell, One University Avenue, Lowell, MA 01854. Email: Mathew_Barlow@uml.edu

1. Introduction

Southwest (SW) Asia, taken here as the region centered on Iran, Afghanistan, and Pakistan, is a generally arid region with high mountains. Except for northern Pakistan, which is influenced by the summer monsoon, precipitation in the region is primarily associated with eastward-moving synoptic storms during the winter and early spring (Martyn 1992). These relatively infrequent storms are of high importance, both in terms of water resources and agriculture and in terms of flooding and transportation hazards. Previous analysis has shown that tropical convection in the eastern Indian Ocean can affect the local winds and precipitation of the region at seasonal time scales during the cold season (Barlow et al. 2002). The tropical convection of the eastern Indian Ocean also has prominent variability on the intraseasonal time scale of the Madden–Julian oscillation (MJO) (e.g., Wang and Rui 1990). This, together with the importance of synoptic time scales in SW Asia precipitation, suggests that important aspects of the relationship between the two regions may be active at daily to intraseasonal time scales. These weather–climate relationships may provide a basis for forecasting at the time scales between synoptic and seasonal.

The climate of SW Asia ranges from desert conditions in interior Iran, southwestern Afghanistan, and southeastern Pakistan, to semiarid steppe in much of the rest of the region, although the vegetation can be relatively lush along the southern shores of the Caspian and in the snowmelt-fed river valleys. In northern Pakistan, the primary rainfall season is summer, associated with the northernmost advance of the Asian monsoon. For the rest of the region, winter and early spring (November–April) is the primary rainfall period (Fig. 1a). The complex terrain of the high mountain ranges in Iran and the Hindu Kush in Afghanistan and northern Pakistan (Fig. 2b) plays a key role in the distribution of precipitation, which is heaviest on the windward slopes. Yearly precipitation can exceed 1 m in some areas of the high mountains of western Iran and the Hindu Kush, as well as along the southern shore of the Caspian Sea (not resolved in the gridded precipitation shown in Fig. 1).

During the Northern Hemisphere winter and spring seasons, SW Asia is within the subtropical belt of upper-air westerlies (e.g., Krishnamurti 1961), which bring the moisture-bearing synoptic storms that are the primary precipitation mechanism for much of the region. The maxima in these westerly winds, or jets, are associated with regions of synoptic storm activity, sometimes called “cyclone belts,” “storm tracks” (Blackmon et al. 1977), or “baroclinic waveguides” (Wallace et al. 1988). A local maximum of synoptic activity exists over the Mediterranean (Petterssen 1958) in association with a baroclinic waveguide (Wallace et al. 1988); occasional extensions of this storm track are associated with the movement of synoptic storms into the region.

On average, SW Asia lies between two maxima in the subtropical westerlies: in the exit region of the North Africa/Arabian jet (nomenclature varies) and near the entrance region of the east Asian jet. The upper-level westerlies have a complex relationship with the storm activity. Shear, deformation, and maximum speed of the upper-level wind field are all known to affect transient activity. Jet exit regions are of particular interest from a number of perspectives. The strong horizontal gradients of wind speed at the entrance and exit regions of the jet are typically balanced by vertical circulations, which can affect the amount and strength of storm activity (Blackmon et al. 1977). The exit region of a jet can serve as a source or sink of energy, via barotropic instability, for transients (e.g., Simmons et al. 1983; Branstator 1985), with the sign of the energy transfer dependent on the horizontal deformation of the transient activity. The jet exit is also a preferred region for baroclinic energy conversion, as the decrease in shear allows the development of local instability modes (Cai and Mak 1990). Indeed, the net effect of the winds on the storm track appears to result from competing contributions from baroclinic and barotropic energetics (Cai and Mak 1990; Whitaker and Dole 1995). The upper-level westerly flow is also reinforced by the diabatic heating of the storms (Hoskins and Valdes 1990), though due to the relatively modest precipitation amounts of SW Asia, it may not be a large factor in this case. Finally, we note that analysis of cold surges, which are also linked with the regional jet structure and tropical rainfall in Indonesia, has suggested interactions between the Asian jet and the North African/Arabian jet (Chang and Lau 1982).

This intermediate position with respect to the local jet features appears to render the local precipitation sensitive to large-scale variability over a wide geographic range. Influences have been identified from Atlantic-based (Aizen et al. 2001) and Pacific-based variability (Nazemosadat and Cordery 2000; Hoerling and Kumar 2003; Tippett et al. 2003), as well as from local SST variability in the Persian Gulf (Nazemosadat 1998). Tropical variability in the eastern Indian Ocean in particular has been identified as effective in forcing the extratropical circulation (Sardeshmukh and Hoskins 1988; Ting and Sardeshmukh 1993) and, specifically, winter precipitation over SW Asia (Barlow et al. 2002). Reduced SW Asian seasonal precipitation occurs in conjunction with enhanced seasonal convection over the eastern Indian Ocean. The eastern Indian Ocean is also a preferred region for MJO activity and, indeed, examination of some previous analyses of MJO evolution does show out-of-phase anomalies between the two regions [e.g., Fig. 3 in Jones et al. (1998) and Fig. 4 in Lo and Hendon (2000)].

The observed pattern of the MJO winds is similar to a convectively coupled Kelvin–Rossby mode (e.g., Rui and Wang 1990; Hendon and Salby 1994). While the dynamics of the MJO are not yet fully understood, this interpretation is supported by several theoretical studies (e.g., Yamagata and Hayashi 1984; Hendon 1988; Wang and Rui 1989; Hendon and Salby 1996; Moskowitz and Bretherton 2000). The two Rossby gyres to the west of the tropical rainfall anomalies have a first-baroclinic-mode structure with opposite-signed circulations at upper and lower levels, and are very similar to the steady linear response expected from the shallow-water equations with no mean wind (Gill 1980). When the MJO tropical rainfall anomalies are in the eastern Indian Ocean, the Northern Hemisphere Rossby gyre extends over SW Asia; thus, the MJO wind anomalies can affect SW Asia in terms of both changes to the upper-level winds as well as the low-level winds (and, hence, moisture transport).

The relationship between the Northern Hemisphere Rossby gyre of the MJO and the midlatitude westerlies has some similarities to the boreal summer analysis of Rodwell and Hoskins (1996, 2001), where, in a simplified model, the Asian monsoon heating forces a Rossby wave response to the west, which interacts with the westerlies to produce descent over the eastern Sahara and Mediterranean and the Kyzylkum desert (encompassing SW Asia, as defined here). Although the Asian monsoon heating is considerably north of the equator while the MJO heating is roughly equatorially symmetric, the summertime westerlies are also displaced northward of the wintertime westerlies and the MJO heating has considerable latitudinal extent, so that the intersection of the Rossby wave response with the westerlies appears similar between the two cases.

The MJO has been shown to modulate synoptic-scale weather and/or its associated precipitation in several regions, including the Americas (Nogues-Paegle and Mo 1997; Mo and Higgins 1998a, b,c; Mo 1999; Jones 2000; Higgins et al. 2000; Nogues-Paegle et al. 2000; Whitaker and Weickmann 2001; Bond and Vecchi 2003), Australia (Hendon and Liebmann 1990; Wheeler and Hendon 2004), and India (Goswami et al. 2003). This modulation, together with demonstrated predictive skill for the MJO out to 3 weeks (Waliser et al. 1999; Lo and Hendon 2000; Wheeler and Weickmann 2001; Mo 2001) and potentially a month (Waliser et al. 2003), suggests the possibility of using the MJO as a basis for predictions at time scales between long-range weather forecasting and short-term climate prediction (Schubert et al. 2002). The nature of the relationship between the MJO and Southwest Asia precipitation, however, and of any related predictability has not yet been explored.

Here we examine this relationship using daily winds, vertical velocity, and outgoing longwave radiation (OLR) for the regional analysis, complemented by daily station precipitation for Afghanistan. The data are described in section 2. The strength and consistency of the precipitation relationship is analyzed in section 3. Changes in dynamic processes, including the jet structure and the thermodynamic balance, are examined in section 4. The potential for predicting SW Asia precipitation based on the MJO is explored in section 5. The importance of accounting for synoptic timing when diagnosing the influence of the MJO is examined in section 6. Finally, a summary and discussion are given in section 7.

2. Data

a. Precipitation estimates

Both OLR and station precipitation data from Afghanistan are used to estimate daily precipitation for November–April, the main precipitation period for much of SW Asia. OLR provides an indirect but relatively long, continuous, and real-time proxy for precipitation, while the station reports provide direct, but short, irregularly reported, and geographically limited data. OLR responds to cloud activity and is frequently used as part of precipitation estimation algorithms. Although OLR is most closely associated with deep tropical convection, it is also related to precipitation in the midlatitudes and at daily time scales (Arkin and Meisner 1987; Huffman et al. 2001). We have used daily station data in the region (described below) to further verify that daily OLR is a useful precipitation proxy. The daily OLR produced by the National Oceanic and Atmospheric Administration–Cooperative Institute for Research in Environmental Sciences (NOAA–CIRES) Climate Diagnostics Center (Liebmann and Smith 1996) at 2.5° × 2.5° latitude–longitude resolution is used for the 1979–2002 period, November–April. Thirteen stations reporting at least 50% of the time are available for 1979–85 for Afghanistan from the Global Summary of the Day (GSOD) (Lott 1998). The station names, locations, elevations, November–April average precipitation, and annual average precipitation are given in Table 1. Although the station data have no formal quality control, examination of consistency with OLR and wind anomalies as well as visual inspection suggests that the quality is satisfactory for the analysis undertaken here, although the number and irregularity of the missing values preclude confident assessment of year-to-year variability in the station data. The daily mean climatology has been removed from the OLR.

b. Winds and vertical velocity

Daily winds and vertical velocity are taken from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996) at 2.5° × 2.5° latitude–longitude resolution for November–April, 1979–2002. The 200-hPa level has proven a useful diagnostic level for both the response to tropical convection anomalies and the jet-level dynamics of synoptic storms, which are the primary precipitation mechanism for much of SW Asia (Martyn 1992). Both the total fields and anomalies are considered; for the anomalies the daily mean climatology for the 1979–2001 period was removed.

c. MJO estimate

The estimates of the MJO are produced by Australia’s Bureau of Meteorology Research Centre (BMRC) based on the technique of Wheeler and Weickmann (2001), which filters daily OLR data for the characteristic eastward-propagating zonal wavenumbers (1–5) and periodicities (30 to 96 days) of the MJO. Two estimates are used: a more accurate “diagnostic” estimate that includes both past and future data in calculating the value for a given time and so cannot be produced in real time, and a “monitoring” or 0-day forecast, which is the estimate that would have been operationally available in real time. For the former we have used the period from 1979 to 2001, and for the latter, the period from 1980 to 1995. An index of MJO convection in the eastern Indian Ocean is created by averaging the data for the rectangular region 15°S–10°N, 80°–100°E.

3. SW Asia precipitation and the sign of the MJO

A very simple measure of the phase of the MJO is the sign, positive or negative, of the associated tropical convection anomaly averaged over a given area, using the Wheeler and Weickmann (2001) data described in the previous section. The period of the MJO is roughly 30–60 days (e.g., Madden and Julian 1994), so it maintains the same sign for 2–3 weeks at a time. We begin our analysis by averaging all the days from November to April 1979–2001 into two groups based on whether the MJO convection anomaly in the eastern Indian Ocean (averaged 15°S–10°N, 80°–100°E) is positive or negative. The averaging region is similar to the “IPX” [Indian Ocean precipitation extension] region of Barlow et al. (2002) but modified slightly to better match the local maximum in MJO activity. The exact definition of the averaging region does not substantially affect the results. For the purposes of this analysis, we refer to enhanced convection (negative OLR anomalies) in the eastern Indian Ocean as the “positive phase” and suppressed convection (positive OLR anomalies) as the “negative phase.” Figure 2 shows the difference in daily OLR and 200-hPa winds between the positive and negative phases of the MJO in the eastern Indian Ocean (red box) for November–April 1979–2001. Here we use the diagnostic MJO information; use of the information that would have been available in real time results in only a modest reduction in signal.

The pattern of OLR and winds in Fig. 2 is consistent with previous research, with upper-level winds having a large response to the north and west of the tropical convection, providing a direct link between the eastern Indian Ocean and SW Asia in the wind field. There is also good correspondence with the steady-state linear response to diabatic heating in the eastern Indian Ocean shown in Barlow et al. (2002). The larger amplitude of the Rossby gyre in the Northern Hemisphere is consistent with the stronger mean winds in the winter hemisphere (Jin and Hoskins 1995). For the negative phase (suppressed tropical convection), the circulation anomalies are opposite in sign. The lower-level winds (not shown) are also consistent with a Rossby response and have a weak circulation opposite to that of the upper-level winds.

Although modest in strength compared to the tropical anomaly, the OLR signal over SW Asia (blue box) has high statistical significance: when the same calculation as for Fig. 2 was based on 1000 randomly generated MJO time series, the same magnitude of signal was obtained less than 0.1% of the time. (The random time series were generated by maintaining the power spectrum of the MJO time series while randomizing the phase of each spectral component. This provides time series that are random but have the same temporal characteristics as the original signal.)

Even though the OLR anomalies over SW Asia appear small, the fact that the average precipitation in the region is also small means that MJO-related anomalies may still represent a significant modulation of the local precipitation. This is confirmed in the station data, where the average over all stations during the negative phase of the MJO (1.66 mm day−1) is increased by 23% relative to the mean, while the average value during the positive phase (1.07 mm day−1) is decreased by 21% relative to the mean. This relationship was also verified separately for the 1979–81 and 1982–85 subperiods of the available station data. This difference is also consistent among the stations (Fig. 3), with 11 of the 13 showing the same relationship, 7 stations showing changes of at least 20%, and 3 stations differing by more than 50% (more than a factor of 2 difference between phases). As expected from the pattern of OLR anomalies in Fig. 2, there is considerable geographic variation in the signal over the country—the largest differences are in those stations on the west and southern flanks of the Hindu Kush (station positions are shown in red in Fig. 1), where the OLR anomalies are also largest, with considerably smaller differences at the other stations, which are in the north and east of Afghanistan. Although the shortness of record and number of missing values in the station data necessitate a cautious interpretation, the available station data suggest that the MJO has a large effect and the OLR anomaly pattern suggests that these Afghanistan station differences may not even reflect the largest part of the signal. Unfortunately, the unavailability of comparable station data in the other countries currently precludes an assessment of this; obtaining more station data will be a focus of future efforts.

Finally, we consider the influence of the MJO with respect to the wettest days in the average over the 13 stations. Of the 10 wettest days, all occurred during the negative phase of the MJO. No 2 of the 10 wettest days occurred within 7 days of each other, so the relationship is not just the result of one or two events. Based on calculations with 1000 MJO-like random time series (generated as described previously), the probability of the 10 wettest days all falling within the same phase by chance is 0.1%.

4. Changes in dynamic process

a. Jet structure

As seen in Fig. 2, the MJO has an effect on the jet-level winds over the region, and so may affect the jet dynamics. The magnitude of the wind field at 200 hPa is shown for the two phases of the MJO in Fig. 4, indicating variations in the jet structure over SW Asia. Although the jet-level winds over SW Asia increase during the positive phase of the MJO, the local precipitation decreases. While the MJO anomalies increase the local wind speed aloft, these anomalies occur in the exit region of the North Africa/Arabian jet maximum and result in a decrease in the gradient of wind speed, producing a more diffuse jet exit region extending from southern Iran through northern India (Fig. 4a). The opposite occurs during the negative MJO, when the negative wind anomalies enhance the jet exit region (Fig. 4b). The presence of an exit region is associated with a local mode of baroclinic instability (Cai and Mak 1990) and so we may expect that an enhanced exit region would be associated with an increase in transient activity and, hence, increased precipitation.

b. Vertical motion and thermodynamic balance

Large-scale vertical velocity can have an important influence on both tropical and extratropical precipitation (e.g., Cotton and Anthes 1989; Bluestein 1993). The changes in vertical velocity associated with the MJO are shown in Fig. 5 at 500 and 300 hPa. Midtropospheric levels are appropriate for diagnosing vertical motions associated with both tropical variability (e.g., Peixoto and Oort 1992) and synoptic storms (e.g., Lim and Wallace 1991). For convenience, the following discussion is with respect to the positive phase of the MJO (enhanced tropical convection in the eastern Indian Ocean and suppressed synoptic precipitation over Southwest Asia); the same arguments apply, with opposite sign, to the negative phase.

As expected, vigorous upward motion occurs in the region of enhanced tropical convection, with largest values at 300 hPa, consistent with previous analysis of vertical velocity associated with Tropical-type convection in the NCEP data (e.g., Barlow et al. 1998). Anomalous subsidence is anticipated over SW Asia, as local precipitation has decreased and this is, indeed, observed. Interestingly, however, the magnitude of the descent is quite large, comparable in size to the tropical anomalies. Given that changes in regional precipitation, while an important fraction of the local average, are modest in terms of absolute value, the vigor of the subsidence is surprising, as is the occurrence of the largest values at 300 hPa. The large values of descent are also considerably in excess of what might be expected by “Hadley”-type forcing from the tropical convection. While the upper-level horizontal winds associated with the MJO (Fig. 2) are in good agreement with the simple shallow-water framework of Gill–Matsuno, the expected descent in the idealized case for equatorially symmetric heating is very diffuse, with largest values only 1/6 of the maximum ascent (Gill 1980), much less than observed here.

What, then, is causing the large values of subsidence? To investigate the dynamics of the descent region, we examine the thermodynamic energy balance. The hydrostatic thermodynamic energy equation may be written (e.g., Holton 1992)
i1520-0493-133-12-3579-eq1
where T is the temperature, V is the horizontal wind vector, Sp is the static stability parameter, cp is the specific heat of dry air, and J represents diabatic heating. Static stability is proportional to the vertical gradient of temperature, so Spω represents the vertical advection of temperature, sometimes called the adiabatic term.

When the tendency term is small with respect to the other terms and the static stability is approximately constant (as assumed in quasi-geostrophy), vertical velocity is in balance with temperature advection and diabatic heating. In the Tropics, scale considerations suggest that vertical velocity is typically balanced by diabatic heating, while in the extratropics vertical velocity is typically balanced by temperature advection (Hoskins and Karoly 1981). In the subtropics, both diabatic heating and temperature advection must be considered (Hoskins 1986). Here we examine the MJO variability with respect to the thermodynamic balance by calculating the terms of the thermodynamic equation from the daily reanalysis data, and then averaging into MJO positive and MJO negative cases and subtracting, as done previously with OLR, winds, and precipitation.

The diabatic heating term is calculated as a residual. Comparisons of diabatic heating residually derived from the NCEP reanalysis and ECMWF reanalysis, and from NCEP reanalysis 6-h model forecasts (Barlow et al. 1998) suggest that, although differences among the estimates indicate some uncertainties in magnitude and vertical profile, they are broadly consistent, particularly at midlevels. The fidelity of modeled circulations forced by residually derived diabatic heating (e.g., Nigam 1994, 1997) lends further credence to the residual calculation. Formally, the thermodynamic equation applies to instantaneous values and frequently is applied to 6-h data for residual diagnosis. Here we are using daily data, so we have validated the calculations by repeating them with one year of 6-h data and compared the resulting MJO composites with those from daily data: they are in close agreement.

Figure 6 shows the difference between positive and negative MJO for each of the four terms in the thermodynamic equation, using the same contour interval throughout. The tendency term (Fig. 6a) is negligible compared with the other terms, so the vertical velocity term (Fig. 6b) is balanced by diabatic heating (Fig. 6c) and temperature advection (Fig. 6d). The similarity between the vertical velocity field (Fig. 5b) and the vertical velocity term (Fig. 6b) shows that static stability is approximately constant. As expected, the tropical balance is largely with diabatic heating, and the extratropical balance is largely with temperature advection, although there is some contribution from diabatic heating. The vigorous upward motion in the tropical eastern Indian Ocean (Fig. 6b) is in balance with the diabatic heating in that area (Fig. 6c), associated with the strong tropical convection of the MJO. The descent over Southwest Asia (Fig. 6b), in contrast, is primarily balanced by temperature advection (Fig. 6d), with only a small contribution from diabatic heating (Fig. 6c).

To further consider the MJO-related changes in temperature advection, we examine the two main contributions: advection of the anomalous (MJO-related) temperature by the mean wind and the advection of the mean temperature by the anomalous (MJO-related) wind. (We have verified that the product of the anomalous terms is small). These are shown in Fig. 7, with the advection shown in (a) and (b) and the component winds and temperatures shown in the (c) and (d). The temperature anomalies associated with the MJO (Fig. 7c) are consistent with the simple Gill–Matsuno framework: the temperature anomalies have approximately the same spatial pattern as the upper-level winds, in keeping with the first-baroclinic-mode structure of the winds and the thermal wind relationship. Both temperature advection terms are important over Southwest Asia, as the vigorous westerlies intersect the extratropical MJO temperature anomalies and the extratropical MJO wind anomalies intersect the mean midlatitude thermal gradient. Both mechanisms force subsidence over Southwest Asia throughout the middle and upper troposphere.

Based on this analysis, we suggest the following interpretation, given for the positive MJO case (signs are reversed for the negative case). Consistent with previous work, the MJO variability is associated with vigorous tropical convection in the eastern Indian Ocean and associated first-baroclinic, Rossby-like wind and temperature anomalies extending over Southwest Asia. The interaction of the MJO circulation and the mean flow results in both advection of the MJO temperature anomalies by the mean wind and advection of the mean thermal gradient by the MJO wind anomalies. Via the thermodynamic energy equation, both these temperature advection terms contribute to subsidence over Southwest Asia, and the subsidence suppresses local precipitation.

A similar argument is made in Rodwell and Hoskins (1996, 2001), in the context of the monsoon heating during boreal summer. Using an idealized model, they show that the Asian monsoon heating produces a Rossby wave packet and the interaction of the westerlies with the warm Rossby thermal anomaly produces descent (“isentropic downgliding”). Although their analysis focuses on boreal summer when the maximum tropical heating is north of the equator, the summertime westerlies are also displaced to the north, and the interaction of the Rossby thermal anomalies with the westerlies appears similar to the present analysis. They also suggest a feedback mechanism whereby the descent, which is occurring in a warm thermal anomaly, would both inhibit precipitation (and associated latent heat release) and increase longwave cooling, factors that would reinforce the descent—a “diabatic enhancement.” There is some suggestion of this with the MJO variability, as a reduction in diabatic heating is observed over Iran (Fig. 6c), although the values are small.

5. Three-week forecast potential

The forecast potential for SW Asia resulting from the MJO relationship depends both on how consistent the relationship is from year to year and on how effectively the relevant part of the MJO can be forecast. For a preliminary assessment, we will use OLR anomalies averaged over SW Asia (blue box in Fig. 2) as the forecast target and a simple persistence scheme for forecasting the sign of the MJO (positive or negative in the red box in Fig. 2).

To examine the consistency of the relationship, the OLR anomalies that occurred during the two MJO phases were calculated for each year and averaged over SW Asia. The yearly anomalies associated with each phase are shown in Fig. 8a, along with the total regionally averaged yearly anomalies (which equals the sum of the two). The influence of the MJO on the regional average is quite consistent from year to year. Additionally, the MJO influence is comparable to the interannual variability: in 9 of the 22 yr the anomalies of one phase of the MJO are of different sign than the seasonal average (black line). That is, in those years, the average influence of the MJO results in wetter-than-average periods even in drier-than-average years, and vice versa.

As a preliminary assessment of the degree to which this relationship might be forecast, we consider an intermediate step where we examine the year-to-year consistency as before but based on a persistence forecast of the sign of the MJO rather than on the observed sign. The time scale of the MJO is roughly 30–60 days (e.g., Madden and Julian 1994) and since a relationship with SW Asia precipitation has been shown based only on the sign of the activity, the influence on the SW Asia precipitation may be expected to last roughly a half-cycle, or about 2–3 weeks at a time. This may provide some useful information in the time scales between daily weather forecasts and seasonal climate predictions. As only the sign of the MJO is being considered here, and given its rather narrow frequency band, a persistence forecast can be made simply by assuming that once the MJO index changes sign, it then maintains the same sign for 3 weeks, either suppressing or enhancing SW Asia precipitation during that time. This approach produces a 21-day forecast every time the MJO index changes sign, resulting in 8–12 forecast periods in the November–April period. This scheme could easily be improved upon but provides a simple starting point. As we are estimating forecast potential, we use the monitoring (0-day forecast) MJO data, the data that would have been available in real time. Figure 8b shows the yearly averages for both the positive and negative cases. Even with this simple scheme, the predicted 3-week periods of enhanced and suppressed OLR show a clear separation between the two phases for 8 of the 16 yr. Moreover, the anomalies for the two cases have opposite signs in 5 of those 8 yr. Obtaining such differences by chance is highly unlikely—in 1000 recalculations of the differences based on an MJO-like random time series the average difference between the two cases never exceeded the observed average difference of −3.42 W m−2.

This result does not represent a formal prediction nor is the forecast target of OLR averaged over all SW Asia expected to be a necessarily useful predictand. However, the demonstrated consistency of the signal in 3-week averages based on just a simple persistence forecast of MJO activity suggests that the forecast potential is worthy of further exploration.

6. Synoptic timing: Three-day evolution

Precipitation in SW Asia comes primarily from the infrequent passage of synoptic storms. So, regardless of the time scale of an external influence, it can only affect local precipitation during the occasional passage of the storms. That is, the influence is only realized during the brief periods of precipitation associated with each storm and is extraneous the rest of the time. Therefore, we can expect the modulation of regional precipitation to be best captured with respect to the timing of the local storms.

A typical track for regional storms passes through southern Iran, Afghanistan, and Pakistan. To examine the influence of the MJO activity in the eastern Indian Ocean on such storms, Fig. 9 shows daily OLR anomaly lags following the incidence of OLR anomaly less than −20 W m−2 in the northern Persian Gulf (red box in Figs. 9a,b). The lags are averaged separately into those cases occurring during a positive MJO in the eastern Indian Ocean (256 cases, shown in Fig. 9a) and those cases during a negative MJO (467 cases, shown in Fig. 9b) for 1979–2001. (The averaging region was chosen based on the 3-day lead OLR composite to the Afghanistan station data; results are not strongly sensitive to the exact definition of the box geography or the threshold.) Despite the same geographic origin and threshold criterion in both cases, the evolution is strikingly different depending on the sign of the MJO. Although the 0-day lags are similar in strength, the 1-day lag shows clear differences, and by the third day, differences are dramatic. Even in the irregularly reported, shorter-record station data, a large difference is present, with Afghanistan precipitation at the 3-day lag twice as large in the negative MJO case compared to the positive case. Especially for the negative MJO case, where the tropical MJO convection anomalies are quite prominent, the characteristic Rossby-like wind response and enhanced vertical motion over SW Asia (not shown) are present both in advance of and during the storm’s movement through the region.

As individual storm tracks and speeds vary considerably, and as only the sign of the MJO is considered, this simple compositing technique provides a somewhat blurred picture. Nonetheless, the timing and pattern of the composite evolution still appears to be relevant to individual events. Figure 9c shows the daily evolution for a synoptic storm associated with severe flooding in Afghanistan for the period 10–13 April 2002 (contour interval is twice that of Figs. 9a,b). Note that these data were not included in the compositing and so provide an independent comparison. Although the event has stronger magnitudes overall, as well as considerably more precipitation in Saudi Arabia than the composites, it has several similarities in timing and pattern, including the position and timing of the maximum tracking through Iran into Afghanistan, as well as the coherence of the MJO signal in the eastern Indian Ocean. A negative MJO composite with magnitudes closer to the April 2002 event can be obtained by compositing with a threshold (the MJO criterion in Fig. 9b was solely based on sign, without accounting for the magnitude of the MJO signal). This finding that the relationship is stronger when the timing of individual storms is considered is consistent with the fact that, as previously noted, all 10 of the wettest days in the station data occur during the negative phase of the MJO. Indeed, compositing for the 3 days previous to the 10 wettest days results in an evolution similar to Fig. 9b: negative anomalies are present throughout the evolution in the eastern Indian Ocean, while positive anomalies start in the northern Persian Gulf and tracking through Afghanistan.

Of particular interest for the April 2002 flooding event, the identifying aspects for the enhanced composite were clearly present 2 days in advance of the flooding in Afghanistan. A synoptic storm was visible tracking over the Persian Gulf on 10 April (Fig. 9c) and the BMRC real-time MJO monitoring data did diagnose a highly negative MJO phase (suppressed convection) in the eastern Indian Ocean (not shown). We emphasize that these identifying aspects for the enhanced case were all present in data that are readily available in real time.

7. Summary and discussion

Using both daily OLR and station precipitation data, we have shown that MJO activity in the eastern Indian Ocean has a considerable influence on the precipitation of SW Asia during November–April. Average daily precipitation, from 13 stations in Afghanistan for 1979–85, decreases by 23% relative to the mean during the periods when the MJO enhances convection in the eastern Indian Ocean (positive phase) and increases by 21% when the MJO suppresses convection in the eastern Indian Ocean (negative phase). Individually, 7 of the 13 stations change by at least 20% and 3 change by more than 50% (more than a factor of 2 difference between the precipitation during the two phases). The distribution of extremes is also affected such that the 10 wettest days all occur during the negative MJO phase. The longer OLR record, 1979–2001, shows that the signal is very consistent, with averaged SW Asia anomalies positive (less precipitation) during the MJO positive phase and negative (more precipitation) during the negative phase for every season in the 1979–2001 record. In 9 of the 22 yr, the average OLR anomaly during one of the phases was of opposite sign with respect to the seasonal anomaly—corresponding to wetter-than-average periods during a drier-than-average year and vice versa.

Dynamically, the MJO-related changes in tropical convection affect both the jet-level winds and the vertical motion at mid- and upper levels. The changes to the upper-level winds affect the sharpness of the jet exit region over the region, consistent with changes in local baroclinic instability and the observed anomalies of precipitation. The vertical motion field is also changed by thermodynamic processes. The interaction of the MJO circulation and the mean flow result in both advection of the MJO temperature anomalies by the mean wind and advection of the mean thermal gradient by the MJO wind anomalies, both terms contributing to vigorous changes in vertical velocity over Southwest Asia, consistent with the changes in precipitation.

The predictability of the MJO and the link between the MJO and SW Asia precipitation suggests the potential for prediction of the SW Asia precipitation. As a preliminary step, we have shown that, for an area average, the MJO is very consistent year to year and a simple persistence forecast shows some ability to distinguish between enhanced and suppressed SW Asia precipitation at 3-week time scales.

The influence of the MJO on SW Asian precipitation can be seen more clearly when keyed to the infrequently occurring local storms. A simple threshold approach using OLR averaged over the northern Persian Gulf (which could be calculated operationally) captures a primary storm track into Afghanistan. Compositing the storm evolution based on the MJO phase shows large differences by the second day, as the storm enters Afghanistan. This MJO influence appears to have relevance to the flooding event of April 2002, when an exceptionally vigorous storm moved through the region in concert with well-defined negative MJO anomalies in the eastern Indian Ocean.

Two factors appear to be important in explaining the sensitivity of the SW Asia precipitation to the MJO: the proximity of SW Asia to the most active region of the MJO, so that SW Asia is within the direct wind response to the MJO tropical convection anomalies when they are at their largest values, and the vigorous wind response to tropical forcing in that same region (Ting and Sardeshmukh 1993). The structure of the mean flow is important both to the vigor of the wind response and to the thermodynamic interaction of the wind response with the mean flow, which results in changes to the vertical velocity over SW Asia. Additional factors, including wave–mean flow interaction and the role of moisture transport, may be important, and further research is warranted.

For future analysis, several key areas can be readily identified. More observational data of precipitation, both in terms of daily station records of precipitation for the other countries of the region and in terms of daily records with more consistent reporting, would be very helpful for better verifying the strength and extent of the signal suggested by the OLR, as well as for analysis of individual events. We are in the process of obtaining such data. A more sophisticated approach to storm tracking would be useful for considering other storm tracks through the region, as well for assessing whether just the strength of the storms is affected by the MJO, or whether the number or track of the storms change as well. Consideration of the degree to which numerical weather prediction model are able to capture the tropical linkages, which appear to be important even to day-2 and day-3 forecasting, would provide a useful test of regional short-term prediction skill and evaluate the utility of considering the MJO state in short-term forecasts. Seasonality needs to be considered, as the mean flow and synoptic activity undergo important changes within the November–April period considered here.

With respect to estimating the MJO variability, there is a considerable amount of both diagnostic and forecasting information not yet made use of. The current analysis only made use of the sign of the MJO; this may be easily extended to account for both the magnitude and life cycle of the MJO, both of which are likely important. For forecasting the MJO, a persistence approach has been used here for simplicity, but a number of considerably more sophisticated schemes have been proposed for MJO forecasting (e.g., Waliser et al. 1999; Lo and Hendon 2000; Mo 2001; Wheeler and Hendon 2004; etc.). A range of monitoring and forecasting estimates are now available or in planning (Schubert et al. 2002); these will allow a more robust estimation of the MJO influence and related forecast skill.

Finally, the underlying dynamics of the relationship need further exploration, including further analysis of the mechanisms outlined here and consideration of their relative importance. Consideration of the dynamical issues with respect to MJO evolution and how the extratropical anomalies change as the MJO propagates from the central Indian Ocean into the western Pacific will likely provide a useful perspective. A key aspect for further analysis is the low-level flow and moisture transport in SW Asia, which is complicated by both data scarcity and the mountainous nature of the region.

Acknowledgments

Interpolated OLR data were produced by the NOAA–CIRES Climate Diagnostics Center, Boulder, Colorado. The IRI data library was invaluable for manipulating the data in this study; all plots were produced with GrADS software. We thank Tony Barnston, Chet Ropelewski, and John Henderson for useful discussions, and David Salstein, Richard Rosen, Jafar Nazemosadat, and the reviewers for thoughtful comments on the manuscript, all of which greatly improved the analysis and presentation. This research was supported by the National Science Foundation under Grant ATM-0233563.

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Fig. 1.
Fig. 1.

(a) Nov–Apr average precipitation, contoured at intervals of 20 cm, from the New et al. (2000) gridded data. Red numbers show station locations for data used in section 3. (b) Topography, contoured at intervals of 1 km.

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 2.
Fig. 2.

Difference in daily OLR and 200-hPa wind anomalies between the positive and negative phases of the MJO in the eastern Indian Ocean (red box), for Nov–Apr 1979–2001. The OLR anomalies are contoured at intervals of 4 W m−2. Negative OLR anomalies, which correspond to positive precipitation anomalies, are shaded green, while positive OLR anomalies, which correspond to negative precipitation anomalies, are shaded brown. As the composite is based on the differences between the two phases from daily data, the average daily anomaly during a particular phase would be half that shown. To estimate the net anomaly resulting from a particular phase, the daily average should be multiplied by the length of the phase (approximately 21 days).

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 3.
Fig. 3.

Afghanistan average daily rainfall (mm day−1) for 13 stations, averaged during positive (red) and negative (blue) phases of the MJO in the eastern Indian Ocean, Nov–Apr 1979–85. All stations have daily reports for at least 50% of the days. The station locations are shown in Fig. 1.

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 4.
Fig. 4.

Composite of 200-hPa wind speed, based on total fields, for (a) the positive phase of the MJO in the eastern Indian Ocean, and (b) the negative phase. The contour interval is 5 m s−1.

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 5.
Fig. 5.

Difference between positive and negative phases of the MJO as in Fig. 2, but for vertical velocity at (a) 500 and (b) 300 hPa. The contour interval is 2 Pa s−1. The vertical velocity is in pressure coordinates, so negative values (shaded blue) represent upward motion and positive values (shaded red) represent downward motion.

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 6.
Fig. 6.

Difference between positive and negative phases of the MJO as in Fig. 2, but for the terms of the thermodynamic equation at 300 hPa: (a) the tendency term, (b) the vertical velocity term, (c) the diabatic heating term, and (d) the temperature advection. The contour interval is 0.3 K day−1 throughout, with the zero contour omitted.

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 7.
Fig. 7.

Difference between positive and negative phases of the MJO as in Fig. 2, but for the primary contributions to temperature advection at 300 hPa. The temperature advection is shown for (a) the advection of anomalous temperature by the mean wind, and (b) the advection of the mean temperature by the anomalous wind. The contour interval is 0.3 K day−1, as in Fig. 6. The associated constituents are shown as (c) the anomalous temperature (shaded) and mean wind (vectors) and (d) the mean wind (contours) and the anomalous winds (vectors).

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 8.
Fig. 8.

(a) The Nov–Apr means of SW Asia (blue box in Fig. 2) OLR anomalies (×−1) are shown as a black line. The Nov–Apr means are also split into the values that occurred during the positive MJO (red line) and the negative MJO (blue line). As the MJO was either positive or negative for every day in the record (no values of exactly zero occurred), the lines corresponding to the two phases sum exactly to the total Nov–Apr anomalies. The sign of the OLR anomalies has been reversed to correspond to the sign of the implied precipitation anomalies. (b) The calculation is repeated, but for 3-week averages following a change in sign of the MJO. This is equivalent to calculating 3-week averages of SW Asia OLR based on a “persistence” forecast of the sign of the MJO.

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Fig. 9.
Fig. 9.

The 4-day evolution of OLR anomalies is shown for (a) 0–3-day lag composites to OLR <−20 W m−2 in western Iran (red box), when the MJO is positive in the eastern Indian Ocean; (b) 0–3-day lag composites to OLR anomalies <−20 W m−2 in western Iran, when the MJO is negative in the eastern Indian Ocean; and (c) daily OLR anomalies for an Afghan flooding event in Apr 2002. Negative OLR anomalies are shaded green, corresponding to positive precipitation anomalies, while positive OLR anomalies are shaded brown, corresponding to negative precipitation anomalies. The contour interval in (a) and (b) is 5 W m−2, and in (c) is 10 W m−2.

Citation: Monthly Weather Review 133, 12; 10.1175/MWR3026.1

Table 1.

Afghanistan stations used in study. The average precipitation values shown were calculated from the daily precipitation values for the 1979–85 period. Station locations are shown in Fig. 1. Note that precipitation in the region is very irregular, and averages during this period may not be representative of other periods.

Table 1.
Save
  • Aizen, E. M., V. B. Aizen, J. M. Melack, T. Nakamura, and T. Ohta, 2001: Precipitation and atmospheric circulation patterns at mid-latitudes of Asia. Int. J. Climatol., 21 , 535556.

    • Search Google Scholar
    • Export Citation
  • Arkin, P., and B. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon. Wea. Rev., 115 , 5174.

    • Search Google Scholar
    • Export Citation
  • Barlow, M., S. Nigam, and E. H. Berbery, 1998: Evolution of the North American monsoon system. J. Climate, 11 , 22382257.

  • Barlow, M., H. Cullen, and B. Lyon, 2002: Drought in central and southwest Asia: La Niña, the warm pool, and Indian Ocean precipitation. J. Climate, 15 , 697700.

    • Search Google Scholar
    • Export Citation
  • Blackmon, M., J. Wallace, N-C. Lau, and S. Mullen, 1977: An observational study of the Northern Hemisphere wintertime circulation. J. Atmos. Sci., 34 , 10401053.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., 1993: Synoptic-Dynamic Meteorology in Midlatitudes. Vol. II, Observations and Theory of Weather Systems, Oxford University Press, 594 pp.

    • Search Google Scholar
    • Export Citation
  • Bond, N., and G. Vecchi, 2003: The influence of the Madden–Julian oscillation on precipitation in Oregon and Washington. Wea. Forecasting, 18 , 600613.

    • Search Google Scholar
    • Export Citation
  • Branstator, G., 1985: Analysis of general circulation model sea surface temperature anomaly simulations using a linear model. Part I: Forced solutions. J. Atmos. Sci., 42 , 22252241.

    • Search Google Scholar
    • Export Citation
  • Cai, M., and M. Mak, 1990: On the basic dynamics of regional cyclogenesis. J. Atmos. Sci., 47 , 14171442.

  • Chang, C-P., and K. M. Lau, 1982: Short-term planetary-scale interactions over the Tropics and midlatitudes during northern winter. Part I: Contrasts between active and inactive periods. Mon. Wea. Rev., 110 , 933946.

    • Search Google Scholar
    • Export Citation
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