The Influence of the Madden–Julian Oscillation on Canadian Wintertime Surface Air Temperature

Hai Lin MRD/ASTD, Environment Canada, Dorval, Québec, Canada

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Gilbert Brunet MRD/ASTD, Environment Canada, Dorval, Québec, Canada

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Abstract

Using the homogenized Canadian historical daily surface air temperature (SAT) for 210 relatively evenly distributed stations across Canada, the lagged composites and probability of the above- and below-normal SAT in Canada for different phases of the Madden–Julian oscillation (MJO) in the winter season are analyzed. Significant positive SAT anomalies and high probability of above-normal events in the central and eastern Canada are found 5–15 days following MJO phase 3, which corresponds to an enhanced precipitation over the Indian Ocean and Maritime Continent and a reduced convective activity near the tropical central Pacific. On the other hand, a positive SAT anomaly appears over a large part of northern and northeastern Canada about 5–15 days after the MJO is detected in phase 7. An analysis of the evolution of the 500-hPa geopotential height and sea level pressure anomalies indicates that the Canadian SAT anomaly is a result of a Rossby wave train associated with the tropical convection anomaly of the MJO. Hence, the MJO phase provides useful information for the extended-range forecast of Canadian winter surface air temperature. This result also provides an important reference for numerical model verifications.

Corresponding author address: Dr. Hai Lin, MRD/ASTD, Environment Canada, 2121 route Trans-canadienne, Dorval, QC H9P 1J3, Canada. Email: hai.lin@ec.gc.ca

Abstract

Using the homogenized Canadian historical daily surface air temperature (SAT) for 210 relatively evenly distributed stations across Canada, the lagged composites and probability of the above- and below-normal SAT in Canada for different phases of the Madden–Julian oscillation (MJO) in the winter season are analyzed. Significant positive SAT anomalies and high probability of above-normal events in the central and eastern Canada are found 5–15 days following MJO phase 3, which corresponds to an enhanced precipitation over the Indian Ocean and Maritime Continent and a reduced convective activity near the tropical central Pacific. On the other hand, a positive SAT anomaly appears over a large part of northern and northeastern Canada about 5–15 days after the MJO is detected in phase 7. An analysis of the evolution of the 500-hPa geopotential height and sea level pressure anomalies indicates that the Canadian SAT anomaly is a result of a Rossby wave train associated with the tropical convection anomaly of the MJO. Hence, the MJO phase provides useful information for the extended-range forecast of Canadian winter surface air temperature. This result also provides an important reference for numerical model verifications.

Corresponding author address: Dr. Hai Lin, MRD/ASTD, Environment Canada, 2121 route Trans-canadienne, Dorval, QC H9P 1J3, Canada. Email: hai.lin@ec.gc.ca

1. Introduction

Subseasonal variations of near-surface air temperature (SAT) in Canadian winter have a significant impact on societal and economical activities. For instance, a persistent extreme warm period is usually associated with freezing rain and ice conditions. A useful forecast beyond about one week of a persistent anomaly in SAT is thus of great value. Unfortunately, numerical weather forecasting models have tremendous difficulties in producing a useful extended-range prediction.

The lack of extended-range forecast skill in a numerical model is largely related to the current level of our understanding of the atmospheric processes that control the subseasonal variability. It is known that in a short range up to about one week, the passage of extratropical cyclones and anticyclones contributes to forecast skill in extratropical regions. On this time range, relatively skillful predictions are usually made using output from numerical weather prediction (NWP) model integrations based on initial conditions. On a seasonal time scale, on the other hand, useful forecasts of the seasonally averaged condition in Canada are also possible. Such forecasts benefit from slowly varying boundary conditions such as the tropical sea surface temperature (SST) anomaly, notably the El Niño–Southern Oscillation (ENSO; e.g., Derome et al. 2001). On intermediate time scales, however, because of the growth of initial errors in the highly nonlinear flow and imperfection of the model, the forecast error becomes so large that a prediction contains little useful information. Such a time scale may be too long to have memory of the initial condition, and too short for a boundary anomaly to take effect. There is indication, however, that the Madden–Julian oscillation (MJO) has a significant impact on the extratropical atmospheric circulation, and thus contributes to subseasonal weather predictions (e.g., Waliser et al. 2003). Whether and how the MJO influences the Canadian weather are not clear. Its mechanism and processes are not well understood and not well represented in numerical models.

The MJO is a tropical large-scale oscillation that is dominated by periods of 30–60 days and zonal wavenumber 1 propagating eastward (Madden and Julian 1971). The MJO organizes convection and precipitation, thus has a direct impact on the weather in the tropics. It has a significant influence on the extratropical atmospheric variability, possibly through Rossby wave propagation (e.g., Ferranti et al. 1990; Matthews et al. 2004), and thus could provide an important signal source for the extratropical weather forecasts on subseasonal time scales. The impact of the MJO on the extratropical circulation and weather has been of considerable interest. For example, Higgins and Mo (1997) investigated the influence of the tropical low-frequency variability on the North Pacific persistent circulation anomalies. Higgins et al. (2000) and Mo and Higgins (1998) illustrated the relationships between tropical convection associated with the MJO and West Coast precipitation. Vecchi and Bond (2004) found that the phase of the MJO has a substantial systematic and spatially coherent effect on subseasonal variability in wintertime surface air temperature in the Arctic region. Donald et al. (2006) reported a near-global impact of the MJO on rainfall.

In this study we compose the connection between the Canadian SAT and the phase of the MJO, and its association with the large-scale atmospheric circulation. In most of the previous studies about the impact of the MJO on midlatitude weather as mentioned above, composites were often made for different phases of the MJO, thus a simultaneous association between the atmospheric variable and the MJO was obtained. In a recent study, Lin et al. (2009, hereafter LBD09) produced lagged composites of the amplitude of the North Atlantic Oscillation (NAO) with respect to different MJO phases. They found a significant increase of the NAO amplitude about 5–15 days after the MJO-related convection anomaly reaches the tropical Indian Ocean and western Pacific. The development of the NAO is associated with a Rossby wave train in the Pacific and North American region. Here we consider the time-lagged association between the Canadian SAT and the MJO phases, as the signal of the tropical diabatic forcing associated the MJO would take about a week to propagate to Canada (Lin et al. 2007a). Such a lagged association would provide useful information for an extended-range MJO-based forecasting of the Canadian SAT even without a forecast of the MJO itself.

Section 2 describes the data used in the analysis. In section 3 lagged composites of the Canadian SAT with respect to the MJO phases are presented. Section 4 discusses the probability of above- and below-normal SAT in Canada in different MJO phases. The time evolution the extratropical flow anomaly associated with two phases of the MJO is discussed in section 5. Conclusions follow in section 6.

2. Data

We make use of the homogenized Canadian historical daily SAT for 210 relatively evenly distributed stations across Canada (Vincent et al. 2002). The data have been adjusted to account for inhomogeneities caused by changes in site exposure, location, instrumentation, observer, and observing procedures. The period of our analysis covers 26 winter seasons from 1979/80 to 2004/05, which is determined by the availability of satellite observed outgoing longwave radiation (OLR) data used to construct the MJO index. The OLR data are continuously available starting from 1979. Missing data in SAT are replaced by a temporal linear interpolation. Stations with more than 20% of missing observations are excluded from the analysis.

To represent the atmospheric circulation fields, the daily data of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (Kalnay et al. 1996) are utilized. Variables used here include geopotential height on 500-hPa (Z500) and sea level pressure (SLP). [These data are provided by the National Oceanic and Atmospheric Administration/Office of Oceanic and Atmospheric Research/Earth System Research Laboratory/Physical Sciences Division (NOAA/OAR/ESRL/PSD) in Boulder, Colorado, from their Web site online at http://www.cdc.noaa.gov/.] To describe the tropical convection associated with the MJO, the pentad data of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997) are used.

The MJO is defined using the Real-Time Multivariate MJO (RMM) index of Wheeler and Hendon (2004). The daily values of RMM1 and RMM2 are obtained from the Australian Bureau of Meteorology Web site (http://www.bom.gov.au/bmrc/clfor/cfstaff/matw/maproom/RMM/). The RMM1 and RMM2 were calculated by projecting the combined fields of 15°S–15°N meridionally averaged OLR and zonal winds at 850 and 200 hPa onto the two leading empirical orthogonal function (EOF) structures as derived using the same meridionally averaged variables. The time series of RMM1 and RMM2 vary mostly on the intraseasonal time scale, and the associated three-dimensional flow structure captures the MJO variability.

The horizontal resolution for the NCEP–NCAR reanalysis and the CMAP precipitation is 2.5° × 2.5°. The daily values of the homogenized SAT station observations, the NCEP–NCAR reanalysis, and the MJO index are averaged for five consecutive days to construct the pentad data. The analysis is conducted for 26 winters from 1979/80 to 2004/05, where the winter is defined to be the months of December, January, and February (DJF), which covers 18 pentads starting from the pentad of 2–6 December to match the pentad definition of the CMAP precipitation, with a total of 468 pentads. The last pentad of each winter always covers a period from 25 February to 1 March, no matter if it is a leap year or not. Thus, in the case of a leap year, the data for that “pentad” is actually an average for 6 days.

The annual cycle of the 26-yr pentad climatology is first removed for each station or grid point. The mean for each winter is then subtracted in order to eliminate interannual variability. The resulting pentad perturbation represents an intraseasonal variability. The seasonal cycle and interannual variability have already been removed in the RMM1 and RMM2 data.

Shown in Fig. 1a,b are the winter climatology and the standard deviation of the pentad-to-pentad variability of SAT in Canada, respectively. To see how much the intraseasonal variability contributes to the total variability, the ratio of the standard deviation of the pentad-to-pentad variability to that of the daily data is illustrated in Fig. 1c. A strong intraseasonal variability of SAT occurs in western Canada to the east of the Canadian Rockies, with a maximum standard deviation of about 8°C. This may be associated with low-frequency penetrations of the winter cold air from the northwest along the east side of the mountains. The intraseasonal variability accounts for most of the total SAT variability in Canada, especially in western Canada.

3. Composites of the Canadian SAT anomalies

a. Phases of the MJO

The RMM index provides a way to describe the MJO with its main characteristics similar to the schematic of Madden and Julian (1971), which consists of convectively coupled, vertically oriented circulation cells propagating eastward around the global along the equator. Eight MJO phases are defined following Wheeler and Hendon (2004) using the RMM1 and RMM2 indices, corresponding to the enhanced convection in different locations of the global tropics. It should be mentioned that the eight phases all have an MJO amplitude (amp = RMM12 + RMM22) greater than 1. Therefore, the weak and inactive MJO cases are excluded from the analysis.

To demonstrate the tropical convective activity associated with each phase of the MJO, shown in Fig. 2 is the simultaneous composites of the CMAP precipitation for different MJO phases. The distributions of precipitation anomalies agree well with the winter composites of the OLR for the MJO phases as presented in Wheeler and Hendon (2004, their Fig. 8). Starting from phase 1, enhanced convection and precipitation develop over Africa and the western Indian Ocean. In the subsequent phases, the positive precipitation anomaly moves eastward along the equator, and gets dissipated in phase 1 over the central Pacific after a whole cycle of the MJO. The maximum precipitation anomaly occurs in phases 3–4 when it is located between the Indian Ocean and the Maritime Continent. When the positive precipitation anomaly reaches the western Pacific in phase 5, a negative anomaly starts to build over Africa and the western Indian Ocean, which repeats the process of its positive counterpart. The enhanced (reduced) convection and precipitation reaches the tropical central Pacific in phases 6–8 (phases 2–4).

Starting from the RMM1 and RMM2 pentad values for the 26 winters from 1979/80 to 2004/05, the pentads that belong to each of the eight phases of the MJO are identified. Listed in Table 1 are the number of pentads and the averaged MJO amplitude for each phase. It can be seen that phases 2–4 and 6–8 have a relative high frequency of appearance. The maximum amplitude of the MJO tends to occur in phases 3–4, which correspond to an enhanced convection over the warm water between the Indian Ocean and the Maritime Continent, consistent with previous studies.

b. The Canadian SAT anomalies

To analyze the connection between the Canadian SAT and the MJO, lagged composites are calculated for the Canadian winter SAT anomaly for different phases of the MJO, that are presented in Fig. 3. Lag 0 represents the simultaneous composite (i.e., the SAT anomaly is for the same pentads that are identified for a given phase of the MJO). The number of pentads for each MJO phase is listed in Table 1. Lag n means that the SAT anomaly composite is for the pentads that lag those with the given MJO phase by n pentads. Areas shaded denote those with a statistical significance level of 0.05 according to a Student’s t test.

Significant SAT anomalies are found mainly in two MJO phase groups. The first group includes phases 3 and 4, whereas the second group consists of phases 7 and 8. For the first phase group, a significant warm SAT anomaly over central and eastern Canada appears one pentad after the MJO is detected in phase 3 (Fig. 3h). The same pattern persists to the next pentad (lag = 2, Fig. 3i). The maximum anomaly appears in Manitoba and Ontario, Canada, and has a magnitude of over 3°C at lag = 1 (Fig. 3h). The SAT anomalies in MJO phase 4 (phase 4, lag = 0, and lag = 1; Figs. 3j,k) can be considered to be associated with the same process, as the MJO phase 4 follows phase 3 in time (by approximately one pentad). Indeed, the similarity between Figs. 3j and 3h and that between Figs. 3k and 3i are evident.

One pentad after the MJO is detected in phase 7, a positive SAT anomaly begins to appear in northwestern Canada (Fig. 3t). This positive anomaly continues to expand eastward. By lag = 2 pentads, a large part of the northern and northeastern Canada is covered by a significant warm temperature anomaly, with a maximum of about 3°C (Fig. 3u). This SAT anomaly pattern continue to evolve for the MJO phase 8. Two pentads after the MJO occurs in phase 8 (Fig. 3x), the positive anomaly in the northeastern Canada disappears and a negative anomaly pattern emerges in southern Canada with a minimum value of about −3°C along the U.S. border.

To assess the importance of the MJO-related SAT anomaly, it is helpful to compare its magnitude with the wintertime pentad-to-pentad standard deviation of SAT. Shown in Fig. 4 are the lagged SAT anomaly composites for MJO phases 3 and 7 divided by the SAT standard deviation, which is illustrated in Fig. 1b. As is seen, for some regions in Canada, the MJO-related SAT anomaly is as strong as about 50%–60% of the wintertime pentad-to-pentad standard deviation.

It is known from previous studies that the extratropical response to a tropical forcing gets fully developed in about two weeks (e.g., Jin and Hoskins 1995; Lin et al. 2007a). This would also be the time scale for the influence of the MJO on Canadian SAT to establish. From Fig. 4, it can be seen that the maximum impact of the MJO on Canadian SAT occurs at a lag of one–three pentads. After that, the SAT anomaly weakens and the area of significant signals reduced. At lag 4, there is almost no significant SAT anomaly.

4. Probability of above- and below-normal SAT in Canada

In this section, we present another measure for the influence of the MJO on the Canadian SAT. For every station, the SATs for all the pentads are categorized as below normal, near normal, and above normal. Pentads with an above-normal SAT are those with an anomaly greater than 0.43 times the standard deviation of pentad-to-pentad variability, whereas pentads with a below-normal SAT are those with an anomaly smaller than −0.43 times the standard deviation, and the rest are categorized as near normal. On average each of the three categories has a probability of 33%.

Here we investigate the probability of above (below) normal SAT in Canada when the MJO is in a given phase, which is also referred to as the probability of the upper (lower) tercile. The probability of the upper (lower) tercile is obtained by counting the number of pentads at each station for which the SAT is above (below) normal, and then dividing by the total number of pentads in that composite. Statistical significance for the probability composite is assessed using a Monte Carlo approach, where we randomly shuffle the order of years for the SAT data and recompute the composite probability. This is repeated 500 times. We then count how many times the probability exceeds that of the actual composite for each station. If less than 5% of the 500 simulations have a probability greater than that of the actual composite, we say that the probability composite passes a 0.05 significance level.

Shown in Fig. 5 are the composites of SAT probabilities for each of the eight MJO phases with lags from 0 to 2 pentads. Contours in red are for probabilities of above normal (upper tercile), while those in blue are for below normal (lower tercile). Areas shaded in orange and green represent those with statistical significance level of 0.05 for these two categories, respectively.

The probability results are in general consistent with the SAT anomaly composites (Fig. 3). A high probability of above-normal SAT occurs over central and eastern Canada in phases 3 and 4. The maximum probability appears in Manitoba and Ontario one–two pentads after phase 3, with a magnitude of 55% ∼ 60%. Following phase 7, a high probability of above-normal SAT is observed in northern and northeastern Canada.

5. Large-scale circulation anomalies

To understand the SAT signal in Canada related to the MJO as observed above and have a better confidence of this connection, in this section we examine the extratropical large-scale circulation anomalies corresponding to the MJO phases. As demonstrated in many previous studies, the MJO with its tropical diabatic heating anomaly can excite a Rossby wave train and influence the extratropical large-scale circulation (e.g., Lau and Phillips 1986; Ferranti et al. 1990; Matthews et al. 2004). The extratropical response to tropical convection, however, is complicated by many factors such as the structure of the background mean flow and interactions with transient eddies (e.g., Ting and Sardeshmukh 1993; Hall and Derome 2000). In LBD09, the tropical–extratropical interaction on the intraseasonal time scale was investigated by focusing on the MJO and the NAO, and a statistically significant two-way connection between the dominant tropical and extratropical modes of variability was found. The development of the NAO after the MJO-related convection anomaly dipole reaches the tropical Indian Ocean and western Pacific region is associated with a Rossby wave train in the upstream Pacific and North American region. Here we look at the Canadian SAT evolution associated with this process.

Shown in Fig. 6 are the lagged composite maps of 500-hPa geopotential height anomaly for MJO phase 3 (Figs. 6a–c) and phase 7 (Figs. 6d–f) in the DJF season. They are very similar to those calculated with the extended winter (November–April) data (Fig. 4 of LBD09). For MJO phase 3, on the simultaneous composite map (Fig. 6a), a dominant feature is that a significant positive height anomaly is formed over the North Pacific centered at 45°N, 180°. Downstream over Alaska and the west coast of Canada, a negative anomaly is formed. One pentad later (Fig. 6b), the wave pattern shows downstream dispersion. By lag +2 (Fig. 6c), significant development of circulation anomalies takes place in the North Atlantic sector, forming a pattern that is similar to a positive NAO. The composite maps for phase 7 (Figs. 6d–f) show a similar evolution as phase 3 except for a reversal of sign, as the tropical forcing changes sign (Fig. 2). As revealed by a wave activity flux analysis in LBD09, a Rossby wave dispersion from the tropical Pacific to extratropical North Atlantic is responsible for the evolution of the large-scale extratropical circulation anomalies after MJO phases 3 and 7.

It can be noticed from Fig. 6 that the extratropical circulation undergoes a dramatic change between lags 1 and 2, when the Rossby wave coming from the Pacific region evolves to develop the NAO pattern. The Rossby wave train is forced by the MJO diabatic forcing. There are also other dynamical processes in play here. For example, the midlatitude synoptic-scale transients help to reinforce the circulation anomaly through vorticity flux convergence (e.g., Held et al. 1989; Lin and Derome 1997). The development of the NAO also involves several dynamical processes as discussed in Lin et al. (2007b), where it was shown that the North Atlantic circulation anomaly develops by extracting kinect energy from both synoptic-scale transients and the mean flow. Therefore, the MJO with its excited Rossby wave train likely acts as a trigger and timer for the NAO development.

As the SLP is highly correlated with surface wind, we here use SLP to describe the character of surface advection. The lagged composite maps of SLP anomaly for MJO phases 3 and 7 are presented in Fig. 7. Compared with Fig. 6, it can be seen that the extratropical large-scale circulation anomalies have an equivalent barotropic structure.

The lagged SAT anomalies for MJO phases 3 and 7 correspond well with those of the 500-hPa height and SLP. For phase 3, the SAT composites of lags 1 and 2 show the warm anomaly in central and eastern Canada (Figs. 3h,i), associated with the positive 500-hPa height anomaly (Figs. 6b,c) and surface warm advection over eastern North America (Figs. 7b,c). For MJO phase 7, the warm SAT anomalies in high-latitude Canada (Figs. 3t,u) correspond to the positive 500-hPa height anomaly in the polar region (Figs. 6e,f) and surface warm advection in northeastern Canada (Figs. 7e,f).

6. Conclusions and discussion

In this study we have examined the impact of the MJO on Canadian surface air temperature (SAT). Based on 26 yr (1979–2005) of pentad data of the homogenized Canadian historical daily surface air temperature and the bivariate MJO index in boreal winter, significant connections between the tropical convection of the MJO and the Canadian SAT are found. The influence of the MJO on the Canadian SAT occurs after the MJO is observed in phases 3–4 and 7–8, when a tropical convection anomaly dipole reaches the eastern Indian Ocean and western Pacific region. A significant above-normal SAT anomaly in central and eastern Canada happens about 5–15 days after the MJO is detected in phase 3, which correspond to an enhanced precipitation over the Indian Ocean and Maritime Continent and a reduced convective activity near the tropical central Pacific. On the other hand, a positive SAT anomaly appears over a large part of the northern and northeastern Canada about 5–15 days after the MJO is detected in phase 7, corresponding to a reduced precipitation over the Indian Ocean and Maritime Continent and an enhanced convective activity near the tropical central Pacific. An analysis of the evolution of the 500-hPa geopotential height and sea level pressure anomalies indicates that the Canadian SAT anomaly is linked to the tropical convection anomaly of the MJO through a Rossby wave train, a process that is associated with the development of the NAO as discussed in LBD09.

The connection between the MJO and the NAO was also reported in Cassou (2008). Zhou and Miller (2005) investigated the relationship between the Arctic Oscillation (AO), which is well correlated with the NAO (Thompson and Wallace 1998), and the MJO in the Northern Hemisphere winter season, and found that a high (low) AO phase tends to be coupled with a strong (suppressed) convective activity associated with the MJO over the Indian Ocean. They argued that the MJO influences the AO polarity by altering the geopotential height anomaly in the North Pacific sector through meridional dispersion of Rossby waves. This result is supported by L’Heureux and Higgins (2008), who analyzed the boreal winter links between the MJO and the AO using observational data and model output.

Previous studies have shown that the extratropical response to tropical forcing usually has a considerable nonlinear component (e.g., Hoerling et al. 1997; Lin and Derome 2004; Lin et al. 2007a). It is interesting to ask whether or not such a nonlinearity exists in the MJO influence on the SAT in Canada. As phases 3 and 7 of the MJO have almost an opposite sign of the tropical convection anomaly, a linear response would require that their corresponding SAT anomalies also have opposite signs. It is not the case if one compares Fig. 3h with Fig. 3t, indicating that the response is not linear.

The influence of the MJO on northern high-latitude wintertime SAT was also analyzed in Vecchi and Bond (2004), where the calculations were made for simultaneous composites of the MJO phases. In general, our results for lag 0 are in agreement with theirs over Canada. The present study demonstrates that with time lags, the signals are much more significant than those in the simultaneous composites. It is known from previous studies that the extratropical response to a tropical forcing gets fully developed in about two weeks (e.g., Jin and Hoskins 1995; Lin et al. 2007a). This would also be the time scale for the influence of the MJO on the extratropical circulation and the NAO to establish.

The time-lagged association between the Canadian SAT and the MJO phases provides important information for an extended-range forecasting of the Canadian SAT. As the connection has a 5–15-day time lag, an extended-range forecast is possible based on the current observed phase of the MJO. Forecasts of even a longer time scale for Canadian SAT may also not be out of reach given that the tropical MJO itself has a potential predictability of several weeks (e.g., Waliser et al. 2003). However, it is important to keep in mind that the composite result presented represents an average influence of many MJO events in a specific phase. A useful forecasting scheme has to be tested using not only the MJO phase information but also its amplitude.

In extended-range forecasting, it is important for a numerical model to have a correct response to tropical heating anomalies. The presented result provides useful information for validating the numerical models.

Acknowledgments

We thank Lucie Vincent of the Meteorological Service of Canada for the provision of the homogenized Canadian surface air temperature, and Dr. Matthew Wheeler of the Centre for Australian Weather and Climate Research at the Bureau of Meteorology for making the MJO index available. We also thank three anonymous reviewers whose comments and suggestions helped to improve our paper. This research was supported in part by the Canadian Foundation for Climate and Atmospheric Sciences and by the Natural Science and Engineering Research Council of Canada.

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  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78 , 25392558.

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    • Export Citation
  • Zhou, S., and A. J. Miller, 2005: The interaction of the Madden–Julian oscillation and the Arctic Oscillation. J. Climate, 18 , 143159.

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    • Export Citation

Fig. 1.
Fig. 1.

(a) Time average, (b) standard deviation of pentad-to-pentad variability, and (c) ratio of intraseasonal to total standard deviation of SAT in Canada during winter (DJF) for the period from 1979/80 to 2004/05. The contour interval is (a) 4°C, (b) 0.5°C, and (c) 0.05. Contours with negative values are dashed. Areas shaded are those with (a) SAT colder than −12°C, (b) standard deviation larger than 6°C, and (c) ratio greater than 0.75.

Citation: Monthly Weather Review 137, 7; 10.1175/2009MWR2831.1

Fig. 2.
Fig. 2.

Simultaneous composites of the CMAP precipitation anomaly in the tropics for different MJO phases. The contour levels for positive values (solid) are 0.5, 1.5, 3.0, and 4.5 mm day−1; while those for negative values (dashed) are −0.5, −1.5, −3.0, and −4.5 mm day−1. Shaded in dark gray are areas with a precipitation rate anomaly greater than 0.5 mm day−1, whereas those in light gray are with a precipitation rate anomaly smaller than −0.5 mm day−1. The phase numbers are marked on the right.

Citation: Monthly Weather Review 137, 7; 10.1175/2009MWR2831.1

Fig. 3.
Fig. 3.

Lagged composites of SAT for different phases of the MJO. The contour interval is 0.5°C. Contours with negative values are dashed. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

Citation: Monthly Weather Review 137, 7; 10.1175/2009MWR2831.1

Fig. 4.
Fig. 4.

Normalized lagged composites of SAT MJO phase (a)–(e) 3 and (f)–(j) 7. The contour interval is 0.1. Contours with negative values are dashed. The contour of zero is omitted. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

Citation: Monthly Weather Review 137, 7; 10.1175/2009MWR2831.1

Fig. 5.
Fig. 5.

Lagged composites of SAT probabilities for different phases of the MJO. Contours in red are for probabilities of above normal (upper tercile), while those in blue are for below normal (lower tercile). Areas shaded in orange and green represent those with statistical significance level of 0.05 for these two categories, respectively.

Citation: Monthly Weather Review 137, 7; 10.1175/2009MWR2831.1

Fig. 6.
Fig. 6.

Lagged composites of 500-hPa geopotential height anomaly for MJO phases (a)–(c) 3 and (d)–(f) 7. The contour interval is 10 m. Contours with negative values are dashed. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

Citation: Monthly Weather Review 137, 7; 10.1175/2009MWR2831.1

Fig. 7.
Fig. 7.

Lagged composites of the SLP anomaly for MJO phase (a)–(c) 3 and (d)–(f) 7. The contour interval is 1 hPa. The contour of zero is omitted. Contours with negative values are dashed. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

Citation: Monthly Weather Review 137, 7; 10.1175/2009MWR2831.1

Table 1.

Number of pentads and averaged amplitude for each phase of the MJO during 26 winters.

Table 1.
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    • Search Google Scholar
    • Export Citation
  • Zhou, S., and A. J. Miller, 2005: The interaction of the Madden–Julian oscillation and the Arctic Oscillation. J. Climate, 18 , 143159.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a) Time average, (b) standard deviation of pentad-to-pentad variability, and (c) ratio of intraseasonal to total standard deviation of SAT in Canada during winter (DJF) for the period from 1979/80 to 2004/05. The contour interval is (a) 4°C, (b) 0.5°C, and (c) 0.05. Contours with negative values are dashed. Areas shaded are those with (a) SAT colder than −12°C, (b) standard deviation larger than 6°C, and (c) ratio greater than 0.75.

  • Fig. 2.

    Simultaneous composites of the CMAP precipitation anomaly in the tropics for different MJO phases. The contour levels for positive values (solid) are 0.5, 1.5, 3.0, and 4.5 mm day−1; while those for negative values (dashed) are −0.5, −1.5, −3.0, and −4.5 mm day−1. Shaded in dark gray are areas with a precipitation rate anomaly greater than 0.5 mm day−1, whereas those in light gray are with a precipitation rate anomaly smaller than −0.5 mm day−1. The phase numbers are marked on the right.

  • Fig. 3.

    Lagged composites of SAT for different phases of the MJO. The contour interval is 0.5°C. Contours with negative values are dashed. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

  • Fig. 4.

    Normalized lagged composites of SAT MJO phase (a)–(e) 3 and (f)–(j) 7. The contour interval is 0.1. Contours with negative values are dashed. The contour of zero is omitted. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

  • Fig. 5.

    Lagged composites of SAT probabilities for different phases of the MJO. Contours in red are for probabilities of above normal (upper tercile), while those in blue are for below normal (lower tercile). Areas shaded in orange and green represent those with statistical significance level of 0.05 for these two categories, respectively.

  • Fig. 6.

    Lagged composites of 500-hPa geopotential height anomaly for MJO phases (a)–(c) 3 and (d)–(f) 7. The contour interval is 10 m. Contours with negative values are dashed. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

  • Fig. 7.

    Lagged composites of the SLP anomaly for MJO phase (a)–(c) 3 and (d)–(f) 7. The contour interval is 1 hPa. The contour of zero is omitted. Contours with negative values are dashed. Shaded areas represent those where the composite anomaly is different from zero at a 0.05 significance level according to a Student’s t test.

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