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  • View in gallery
    Fig. 1.

    Examples of ARs following MJO phase 3 impacting (a) the Pacific Northwest and (b) Alaska. Each panel shows the OLR anomaly (shading south of 18°N; W m−2) when the RMM phase is 3 (the date is on the upper right), 500-hPa geopotential height anomaly (shading north of 20°N; m), and IVT anomaly (red contours; intervals of 100 kg m−1 s−1 starting at 200 kg m−1 s−1 with a thick line). The dates of geopotential height and IVT are 6 days after the phase 3 dates. The yellow grid indicates the location of the Pacific Northwest region defined in this study.

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    Fig. 2.

    Histogram (%) of MJO events with and without ARs as a function of (a) month and (b) ENSO condition.

  • View in gallery
    Fig. 3.

    Composite longitude–time diagrams of (a) 500-hPa geopotential height anomaly (m), and (b) 250-hPa meridional wind anomaly (m s−1) averaged over 30°–60°N for the 96 MJO events. (c) Composites of 5-day running-mean IVT anomaly (kg m−1 s−1) over the Pacific Northwest for the 96 MJO events. Time 0 is when MJO initially becomes RMM phase 3. Gray and black stippling denotes where anomalies are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

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    Fig. 4.

    Statistics of MJO events with and without ARs based on the RMM indices. (a) Composites of RMM amplitudes for MJO events with and without ARs during lags 0–19 days. (b) Histograms (%) of phase 3 duration (day), (c) percentage of MJO events that reached (Exist) or did not reach (Not exist) phase 6 within 40 days following the last day of phase 3, and (d) number of days to propagate from the last day of phase 3 to the first day of phase 6 for MJO events with and without ARs.

  • View in gallery
    Fig. 5.

    Composite longitude–time diagrams of 500-hPa geopotential height anomaly (m) averaged over 30°–60°N for MJO events (a) with ARs and (b) without ARs, and (c) the difference between (a) and (b). (d) Composites of 5-day running-mean IVT anomaly (kg m−1 s−1) over the Pacific Northwest for events with and without ARs. Gray stippling in (a)–(c) and circles in (d) denote statistical significance at the 5% level. Black stippling in (a)–(c) indicates significance accounting for the false discovery rate.

  • View in gallery
    Fig. 6.

    Composites of pentad-mean IVT anomaly (kg m−1 s−1) for MJO events (a)–(c) with ARs and (d)–(f) without ARs for (top) 0–4, (middle) 5–9, and (bottom) 10–14 days.

  • View in gallery
    Fig. 7.

    Composites of (a) MJO regressed and (b) residual 500-hPa geopotential height anomaly (shading; m) and 250-hPa meridional wind anomaly (contours; interval of 1 m s−1) for the 96 MJO events at lags 0–10 days. Color shading in (a) is only shown where height anomalies are statistically significant considering the false discovery rate. Gray and black stippling in (b) denote where height anomalies are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

  • View in gallery
    Fig. 8.

    As in Fig. 7, but for IVT anomaly (kg m−1 s−1).

  • View in gallery
    Fig. 9.

    Composites of (a) MJO regressed and (b) residual geopotential height anomaly (shading; m) at 500 hPa for MJO events (left) with ARs and (right) without ARs at lag 0 days. The geopotential height field is partitioned into 10-day high-pass (<10 days) and 10-day low-pass (>10 days) fields. Lines in (b) are drawn by connecting the local extrema in the high-pass geopotential height anomaly. Color shading in (a) indicates statistical significance considering the false discovery rate. Gray and black stippling in (b) denote where anomalies/differences are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

  • View in gallery
    Fig. 10.

    Composites of residual IVT anomaly (kg m−1 s−1) for MJO events (a)–(c) with ARs and (d)–(f) without ARs at lags of (top) 0, (middle) 5, and (bottom) 10 days. Gray and black stippling denote where anomalies are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

  • View in gallery
    Fig. A1.

    Scatterplots of quantiles of maximum regional-averaged IVT during lags 3–19 days (x axis) and quantiles of gridded IVT during lags 3–19 days for the 96 MJO events (y axis). Quantiles of gridded IVT are averaged over the top three grid points. The vertical and horizontal lines correspond to 0.95 and 0.98 quantiles, respectively.

  • View in gallery
    Fig. B1.

    Number of MJO events (gray; left axis) and AR events (blue; right axis) as a function of month.

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What Distinguishes MJO Events Associated with Atmospheric Rivers?

Kinya TorideaDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington
bInstitute of Industrial Science, The University of Tokyo, Kashiwa, Chiba, Japan

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Gregory J. HakimaDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington

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Abstract

Atmospheric rivers (ARs) are long and narrow bands of intense water vapor transport that often induce heavy precipitation and flooding along the west coast of North America. Previous studies demonstrate that the Madden–Julian oscillation (MJO) contributes to the predictability of AR activity on a subseasonal time scale. However, individual AR responses vary widely following the same phase of MJO. Here we investigate 96 MJO events following phase 3 (convection over the Indian Ocean) and analyze the differences between events that brought large-scale ARs to the entire Pacific Northwest coastal region and those that did not. The MJO events with ARs tend to stay longer at phase 3 than those without ARs. We use linear regression to isolate the extratropical signals linearly related to the MJO and explore how the residual fields differ between the two groups. The linearly dependent signal shows slow-moving planetary-scale water vapor transport across the North Pacific. The residual field exhibits a contrasting extratropical low-frequency (>10 days) pattern in the Gulf of Alaska resembling the Pacific–North American (PNA) pattern: a trough for events with ARs and a ridge for events without ARs. This low-frequency variability alters the Pacific waveguide such that, for MJO events with ARs, baroclinic waves are directed into the Pacific Northwest with an orientation that favors anomalous positive IVT. For MJO events without ARs, the ridge strengthens large-scale anticyclonic flow over the North Pacific and prevents moisture access to the Pacific Northwest.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kinya Toride, kinyat@uw.edu

Abstract

Atmospheric rivers (ARs) are long and narrow bands of intense water vapor transport that often induce heavy precipitation and flooding along the west coast of North America. Previous studies demonstrate that the Madden–Julian oscillation (MJO) contributes to the predictability of AR activity on a subseasonal time scale. However, individual AR responses vary widely following the same phase of MJO. Here we investigate 96 MJO events following phase 3 (convection over the Indian Ocean) and analyze the differences between events that brought large-scale ARs to the entire Pacific Northwest coastal region and those that did not. The MJO events with ARs tend to stay longer at phase 3 than those without ARs. We use linear regression to isolate the extratropical signals linearly related to the MJO and explore how the residual fields differ between the two groups. The linearly dependent signal shows slow-moving planetary-scale water vapor transport across the North Pacific. The residual field exhibits a contrasting extratropical low-frequency (>10 days) pattern in the Gulf of Alaska resembling the Pacific–North American (PNA) pattern: a trough for events with ARs and a ridge for events without ARs. This low-frequency variability alters the Pacific waveguide such that, for MJO events with ARs, baroclinic waves are directed into the Pacific Northwest with an orientation that favors anomalous positive IVT. For MJO events without ARs, the ridge strengthens large-scale anticyclonic flow over the North Pacific and prevents moisture access to the Pacific Northwest.

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Corresponding author: Kinya Toride, kinyat@uw.edu

1. Introduction

The west coast of North America is subject to extreme precipitation events in the winter season due to filament-shaped water vapor transport, called atmospheric rivers (ARs). In addition to hazards associated with heavy precipitation, ARs are also a primary mechanism for meridional water vapor transport in the midlatitudes (Zhu and Newell 1998; Newman et al. 2012). They represent moisture convergence zones along the cold fronts of extratropical cyclones (Ralph et al. 2018), and four or five ARs are usually observed at any time in each hemisphere (Zhu and Newell 1998). ARs have a significant impact on water resources and can cause disastrous floods in many coastal regions, including the west coast of North America (e.g., Dettinger et al. 2011; Warner et al. 2012; Toride et al. 2018), South America (Viale et al. 2018), Europe (Lavers et al. 2011; Ramos et al. 2015), Africa (Blamey et al. 2018), and East Asia (Kamae et al. 2017).

On subseasonal time scales, AR activity is modulated by large-scale atmospheric/oceanic conditions over the tropics, such as the Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972). Tropical heating anomalies due to the MJO cause upper-level divergence and affect midlatitude weather by generating Rossby waves that radiate toward midlatitudes (e.g., Hoskins and Karoly 1981; Sardeshmukh and Hoskins 1988; Moore et al. 2010). Previous studies have linked MJO and AR activity along the western coastline of North America. Ralph et al. (2011) conducted a detailed case study on an AR event during March 2005 and pointed to possible AR triggering mechanisms by MJO propagation and extratropical wave packets. Guan et al. (2012) analyzed 84 AR events during 1998–2010 and showed that high-impact ARs (defined based on Sierra Nevada snowpack) tend to appear during active MJO phase 6. Subsequent research confirms MJO modulation on ARs throughout the MJO life cycle by various statistical approaches (Payne and Magnusdottir 2014; Guan and Waliser 2015; Mundhenk et al. 2016a, 2018; Baggett et al. 2017; Zhou et al. 2021; Toride and Hakim 2021).

Several factors could influence MJO teleconnections to the extratropical circulation, such as the midlatitude basic state and MJO heating characteristics (e.g., Henderson et al. 2017; Zhou et al. 2020). For instance, midlatitude basic-state changes associated with ENSO modulate the MJO teleconnection pattern. In La Niña years, the Pacific jet stream is displaced northward, and a large-scale anticyclonic anomaly occupies the entire North Pacific when the MJO convection develops over the Indian Ocean (Moon et al. 2011; Lee et al. 2019; Tseng et al. 2020b). In addition, the speed, intensity, location, and lifetime of MJO heating can modify the extratropical response. Yadav and Straus (2017) discuss differing teleconnection patterns due to the differing propagation speeds of MJO. Chen (2021) shows the diversity of MJO teleconnection patterns using four MJO types (fast, slow, jumping, and standing) classified by K-means cluster analysis. Other results demonstrate that information about the quasi-biennial oscillation (QBO) also serves to extend AR forecasts based on MJO (Baggett et al. 2017; Mundhenk et al. 2018) because MJO amplitude covaries with the QBO phase (Yoo and Son 2016).

Recently, Toride and Hakim (2021) demonstrated that AR forecasts over the Pacific Northwest could be further improved by combining MJO phase information with the monthly Pacific–North American (PNA; Wallace and Gutzler 1981) index. Low-frequency (∼30 days) PNA variability influences the MJO–AR relationship much more robustly than ENSO and QBO by controlling the zonal scale of the Pacific jet and baroclinic wave packets, indicating the dominant role of the midlatitude basic state for the AR response to MJO events. They further show that low-frequency PNA variability has no apparent relationship with the MJO, so that the effects of PNA on AR events can be recovered by linear superimposition with the MJO teleconnection patterns. This result suggests that low-frequency extratropical variability linearly independent of MJO is responsible for AR activity over the west coast of North America.

The goal of this study is to identify characteristics of the MJO and extratropical processes that could lead to differences in North American AR activity. We analyze 96 MJO event composites following MJO phase 3 (convection over the Indian Ocean), after which AR activity increases about 10 days later over the Pacific Northwest. This method focuses on the AR response to individual MJO episodes identified based on residing in phase 3 for three consecutive days, allowing for the different evolution of individual MJO events. This approach differs from time-lagged analysis from a particular MJO phase (as in many previous studies) since each MJO event usually stays at each phase for ∼5 days, resulting in multiple samples of each individual MJO event. So far, the AR response to propagating MJO events has been only analyzed for a single event during March 2005 (Ralph et al. 2011).

These MJO events are then divided into cases that brought large-scale ARs to the entire Pacific Northwest coastal region and events that did not. The MJO events with ARs are found to have a longer residence time at phase 3 than the MJO events without ARs. Furthermore, we use linear regression to remove the signal linearly related to the MJO and analyze the differences in the residuals (i.e., the part not linearly explainable by the MJO). The residual component reveals an out-of-phase extratropical low-frequency pattern between the MJO events with and without ARs. This extratropical signal resembles the PNA pattern and guides high-frequency midlatitude baroclinic waves consistent with the finding of Toride and Hakim (2021). We also demonstrate that the regressed and residual components exhibit a nearly equal amplitude of North Pacific geopotential height, suggesting an essential role of extratropical dynamics in addition to MJO tropical–extratropical teleconnections in North American AR activity.

2. Methods

a. Data

Daily mean fields of horizontal winds and geopotential height on isobaric surfaces are obtained from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) (Gelaro et al. 2017). Precalculated integrated vapor transport (IVT) fields are obtained from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al. 2018), which uses the horizontal wind components and specific humidity from 1000 to 200 hPa of MERRA-2. Daily outgoing longwave radiation (OLR) from the National Oceanic and Atmospheric Administration (NOAA) (Liebmann and Smith 1996) is used as a proxy for tropical convection. We focus on extended boreal wintertime [November to April (NDJFMA)] through the years 1980–2019. Each dataset is interpolated to a 2.5° × 2.5° grid. The anomaly field is calculated by subtracting the time mean and the first four harmonics of the annual cycle.

b. MJO

MJO activity is defined by the Real-Time Multivariate MJO (RMM) indices (Wheeler and Hendon 2004). The two RMM indices (RMM1 and RMM2) are the principal components of the two leading empirical orthogonal functions of standardized 200-hPa zonal wind, 850-hPa zonal wind, and OLR anomalies averaged within 15° of the equator. An MJO cycle is divided into eight phases by tan−1(RMM2/RMM1), with phase 1 corresponding to convection over the western Indian Ocean, and phase 8 corresponding to convection over the eastern Pacific. Here we define MJO events as those that exceed MJO amplitude 1.0, defined by (RMM12+RMM22), for three consecutive days in phase 3, similar to Yadav and Straus (2017). We also require the event interval to be greater than 20 days to ensure the intraseasonal time scale of the MJO. If there is more than one event within a 20-day running window, the one with the latest initiation time is kept. This definition yields 96 MJO events in NDJFMA during the years 1980–2019. MJO phase 3 is when convection is over the Indian Ocean and followed by a large-scale ridge over the North Pacific with strengthened midlatitude Rossby wave packets (Cassou 2008). The AR activity composite over the Pacific Northwest significantly increases at lag 1–12 days following phase 3, peaking at around lag 10 days (Toride and Hakim 2021).

Figure 1 shows two contrasting examples of MJO events in February 1996 and January 2002. Both events are aligned by the starting date of phase 3. The AR event in February 1996 exhibits a commonly recognized AR pattern, the so-called Pineapple Express, where narrow plumes of water vapor extend poleward from the Hawaiian Islands (e.g., Colle and Mass 2000). In contrast, a strong ridge develops over the Gulf of Alaska in January 2002, where the anticyclonic flow suppresses AR activity over the Pacific Northwest. The region of intense water vapor transport is directed northward into the coast of Alaska. These illustrative examples show that individual AR responses vary widely following the same phase of MJO.

Fig. 1.
Fig. 1.

Examples of ARs following MJO phase 3 impacting (a) the Pacific Northwest and (b) Alaska. Each panel shows the OLR anomaly (shading south of 18°N; W m−2) when the RMM phase is 3 (the date is on the upper right), 500-hPa geopotential height anomaly (shading north of 20°N; m), and IVT anomaly (red contours; intervals of 100 kg m−1 s−1 starting at 200 kg m−1 s−1 with a thick line). The dates of geopotential height and IVT are 6 days after the phase 3 dates. The yellow grid indicates the location of the Pacific Northwest region defined in this study.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

We use linear regression to isolate the extratropical signals linearly related to the MJO. A time series of an extratropical field x is decomposed to regressed and residual components at each point on the latitude–longitude grid:
x(t)=xregressed(t)+xresidual(t),
where xregressed is defined by regressing x onto a linear combination of RMM indices:
xregressed(t)=aRMM1(t)+bRMM2(t)+c.
Here, a and b are regression coefficients and c is the intercept (constant). The regression coefficients and intercept are estimated by the least squares fit during NDJFMA, 1980–2019, at each grid point.

Although linear methods might underestimate the effects of nonlinear tropical–extratropical interactions, previous studies have shown that the response to eastward-propagating MJO heating can be reasonably reproduced by a linear model (e.g., Mori and Watanabe 2008; Tseng et al. 2020b; Zhou et al. 2020). Similar linear approaches to extract the MJO influence have been employed in previous studies (e.g., Zhang and Gottschalck 2002; Adames and Wallace 2014; Yao et al. 2015). Tseng et al. (2020a) show that the inclusion of the past 15 days of MJO phases as additional predictors in the regression helps in capturing the accumulated influence of past MJO activity for some MJO phases and lead times. We note that the results are not sensitive to the inclusion of past MJO phases in this study. Since the RMM indices are dominated by intraseasonal variability (Wheeler and Hendon 2004), regressed extratropical fields are also expected to be large-scale features with intraseasonal time scales.

c. Atmospheric rivers

This study focuses on AR activity over the Pacific Northwest, defined by the 10 yellow grid points in Fig. 1. Anomalous IVT averaged over the Pacific Northwest region is used as a proxy for AR activity. ARs are defined by days in which anomalous IVT averaged across the yellow region exceeds the 95th percentile of NDJFMA IVT (143 kg m−1 s−1). This method does not require specific geometry of moisture plumes (length, width, or orientation) as in many other studies [summarized in Shields et al. (2018) and Rutz et al. (2019)]. This approach only requires strong vapor transport reaching the coast, but generally yields consistent results in detecting landfalling ARs compared with other AR detection methods, according to Warner and Mass (2017). We use IVT values averaged over the 10 grid points to detect ARs, while thin ARs could impact part of the region where such events might not exceed the 95th percentile for the regional average. Thus, our definition focuses on hazardous ARs that affect the entire Pacific Northwest coastal region (Ralph et al. 2019). The difference in quantiles of IVT between a grid-based method and regional-averaged method is discussed in appendix A.

We divide the 96 MJO events into those that brought ARs to the Pacific Northwest at least one day during lags 3–19 days (MJO events with ARs) and those that did not (MJO events without ARs), resulting in 51 events with ARs and 45 events without ARs. The selected time window starts after the three consecutive days of phase 3 during lag 0–2 days and ends before the anomalous IVT composite becomes negative over the Pacific Northwest (see Fig. 1d of Toride and Hakim 2021). This relatively wide time window covers ARs that affect the Pacific Northwest region with different timing relative to the starting point to incorporate various extratropical responses due to different MJOs. We note that the main findings of this paper are insensitive to the use of a shorter time window (e.g., 3–12 days as compared to 3–19 days; not shown). Figure 2a illustrates the seasonal distribution of these events. MJO events with ARs frequently occur in early winter (November and December), while events without ARs dominate later months (March and April) due to the decreased number of AR events in Pacific Northwest (appendix B; Payne and Magnusdottir 2014). Note that strong water vapor transport also appears in the events without ARs but is directed more toward the Alaska region, rather than the Pacific Northwest (shown later in Fig. 6).

Fig. 2.
Fig. 2.

Histogram (%) of MJO events with and without ARs as a function of (a) month and (b) ENSO condition.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

Figure 2b shows the ENSO phase dependence of the MJO events with and without ARs. Here, El Niño and La Niña periods are defined based on a threshold of ±0.5°C for the oceanic Niño index (ONI), which is a 3-month running mean of sea surface temperature anomalies in the Niño-3.4 region (5°S–5°N, 170°–120°W). Prolonged warmer than normal sea surface temperature in the eastern tropical Pacific Ocean during El Niño often supplies enhanced moisture for transport to the west coast of North America (Ryoo et al. 2013; Guan and Waliser 2015). This tendency is consistent as seen from the increased number of MJO events with ARs during El Niño. However, we present the results with all 96 MJO events without conditioning on the ENSO phase in this paper as our main conclusions do not change when considering only MJO events during La Niña and Neutral conditions, where the numbers of MJO events with and without ARs are similar (not shown).

d. Statistical significance

A two-sided Student’s t test is used to assess the statistical significance of composite anomalies at the 5% level for total, regressed, and residual fields. The sample size is 96 for all MJO events, 51 for MJO events with ARs, and 45 for MJO events without ARs. To test the statistical significance of the difference between the two groups, we perform a two-sided permutation test (resampling without replacement) using the 96 samples. The null hypothesis is that the true difference is zero, which is rejected at the significance level of 5%. The null distribution is constructed with 10 000 permutations, and the 2.5th and 97.5th percentiles of the null distribution are used as the thresholds.

When multiple hypotheses are simultaneously tested, as for a map of gridded data, Wilks (2016) recommends adjusting the threshold p value for the number of false discoveries. We use the Benjamini and Hochberg step-up procedure (Benjamini and Hochberg 1995) with 20% false discovery rate (FDR). However, as relying only on the FDR-based results could also lead to misinterpretation of results (Higdon et al. 2008), we present results for both the local tests and the tests accounting for the FDR.

3. MJO events following phase 3

a. All MJO events

Figure 3 shows the lag composites of the 96 MJO events aligned by the starting date of MJO phase 3. The most significant response in the extratropical geopotential height field is the development of a large-scale ridge over the central Pacific that lasts over 15 days (Fig. 3a). The downstream development of a baroclinic wave packet is also apparent, especially in the meridional wind field (Fig. 3b). The trough around 120°W in the 500-hPa geopotential height field and southerly winds around the west coast of North America during lag 5–10 days indicate favorable conditions for ARs.

Fig. 3.
Fig. 3.

Composite longitude–time diagrams of (a) 500-hPa geopotential height anomaly (m), and (b) 250-hPa meridional wind anomaly (m s−1) averaged over 30°–60°N for the 96 MJO events. (c) Composites of 5-day running-mean IVT anomaly (kg m−1 s−1) over the Pacific Northwest for the 96 MJO events. Time 0 is when MJO initially becomes RMM phase 3. Gray and black stippling denotes where anomalies are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

The averaged IVT anomaly over the Pacific Northwest is shown in Fig. 3c. The decreased AR activity during MJO phase 3 is consistent with previous findings (Mundhenk et al. 2016a). The IVT anomaly starts to increase after lag 3–4 days and peaks at lag 11 days. However, the increased IVT anomalies are not statistically significant (p value of 0.08 at lag 11 days), reflecting the high variability of AR activity following MJO phase 3.

b. Characteristics of MJO events with and without ARs

To investigate the characteristics of eastward-moving MJO events, we analyze the RMM amplitude, the number of days during phase 3, and the number of days to propagate from phases 3 to 6 following Yadav and Straus (2017). Figure 4a shows that the mean RMM amplitude of the MJO events without ARs is larger at the initial stage but decreases earlier than that of the MJO events with ARs. However, the difference in RMM amplitude is not statistically significant during lags 0–19 days according to the permutation test. The convective heating of MJO events with ARs stays significantly longer over the Indian Ocean (phase 3), with a peak duration of around 6 days (Fig. 4b). While AR activity over the Pacific Northwest decreases during MJO phase 3, we speculate that the longer MJO residence time over the Indian Ocean may be responsible for triggering an extratropical response that favors ARs. Figure 4c shows the existence of phase 6 with amplitude larger than 1 for multiple days for MJO events with and without ARs. MJO phase 6 corresponds to when AR activity increases over the Pacific Northwest on average (Guan and Waliser 2015; Mundhenk et al. 2016a; Zhou et al. 2021). Although phase 6 occurs with 76% (39/51 = 76%) of MJO events with ARs, it also occurs with 67% (30/45) of those without ARs. This suggests that MJO events without ARs have different extratropical states than the composite based on the MJO phase. The propagation speed from phases 3 to 6 is similar between the two groups, peaked around 10–15 days (Fig. 4d).

Fig. 4.
Fig. 4.

Statistics of MJO events with and without ARs based on the RMM indices. (a) Composites of RMM amplitudes for MJO events with and without ARs during lags 0–19 days. (b) Histograms (%) of phase 3 duration (day), (c) percentage of MJO events that reached (Exist) or did not reach (Not exist) phase 6 within 40 days following the last day of phase 3, and (d) number of days to propagate from the last day of phase 3 to the first day of phase 6 for MJO events with and without ARs.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

To summarize, the statistically significant difference between the events with and without ARs is mainly in the time duration of convective activity over the Indian Ocean, which could alter the propagation range of MJO heating (Zhang and Ling 2017) and the subsequent extratropical response (e.g., Henderson et al. 2017; Zhou et al. 2020). However, it does not appear to be the only factor differentiating events with and without ARs. For instance, the trough around 120°W (Fig. 3a) is not found in a conditional sample of MJO events based on longer lifetimes defined by RMM amplitude being greater than 1 for at least 10 days (not shown). This suggests the importance of the midlatitude basic state in controlling AR activity, which is discussed in the following sections.

c. Extratropical response to MJO events with and without ARs

Figure 5 shows geopotential height and IVT composites of the MJO events with and without ARs. A robust and consistent spatial shift in trough and ridge locations can be identified between the two groups. In the cases with ARs, the large-scale ridge over the central Pacific is weaker than the composite of all events, and a trough develops over the Gulf of Alaska (Fig. 5a). In contrast, in the cases without ARs, the large-scale ridge is stronger and extends to the east, which shifts the downstream trough eastward (Fig. 5b). A large ridge located over the Gulf of Alaska supports meridional flow toward Alaska (Mundhenk et al. 2016b). The difference (Fig. 5c) reveals that the phase reversal between the two groups is statistically significant from 0 to 20 days with maximum amplitudes over the Gulf of Alaska and western United States. Consistent with the pattern in the height field, IVT anomalies are significantly positive during lags 9–18 days for the events with ARs, and significantly negative during lags 13–19 days for the events without ARs (Fig. 5d).

Fig. 5.
Fig. 5.

Composite longitude–time diagrams of 500-hPa geopotential height anomaly (m) averaged over 30°–60°N for MJO events (a) with ARs and (b) without ARs, and (c) the difference between (a) and (b). (d) Composites of 5-day running-mean IVT anomaly (kg m−1 s−1) over the Pacific Northwest for events with and without ARs. Gray stippling in (a)–(c) and circles in (d) denote statistical significance at the 5% level. Black stippling in (a)–(c) indicates significance accounting for the false discovery rate.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

The spatial structure of moisture flux is shown in Fig. 6. Here we examine the pentad-mean IVT anomaly because of the dominant, nearly stationary, signal in the geopotential height results. The positive vapor flux over the eastern Pacific (Fig. 6a) develops and reaches the Pacific Northwest coast (Figs. 6b,c) for the MJO events with ARs. The vapor flux is disconnected from the large-scale moisture flux over the western Pacific due to the strong cyclonic flow associated with the trough over the Gulf of Alaska. During 10–14 days, the cyclonic flow from the north and anticyclonic flow from the south converge over the Pacific Northwest and support intense moisture flux to the coast (Fig. 6c).

Fig. 6.
Fig. 6.

Composites of pentad-mean IVT anomaly (kg m−1 s−1) for MJO events (a)–(c) with ARs and (d)–(f) without ARs for (top) 0–4, (middle) 5–9, and (bottom) 10–14 days.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

In contrast, for the MJO events without ARs, the large-scale vapor flux develops farther north across the North Pacific (Fig. 6d). The vapor flux is directed northward, reaching Alaska during 10–14 days (Fig. 6f), due to the anticyclonic flow around the large-scale ridge over the Pacific (Fig. 5b), whereas the moisture access to the Pacific Northwest is suppressed in response to anomalous northerly flow.

The contrasting patterns of moisture flux resemble Fig. 12 of Ryoo et al. (2013), where they compared the IVT composites in four La Niña years and four El Niño years. The anticyclonic flow in the La Niña years is roughly similar to the cases without ARs, and the cyclonic flow in the El Niño years is similar to those with ARs. These patterns are also evident for a sample of MJO events that exclude those during El Niño (not shown), indicating that ENSO phase is not only the contributing factor, and that others, such as the PNA, also contribute (e.g., Toride and Hakim 2021).

4. MJO regressed and residual fields

The statistical association between the MJO and North American AR events is noisy, motivating the use of other events and indices (e.g., ENSO, QBO, and PNA; Toride and Hakim 2021) to refine the relationship. Here we take a different approach by separating the signals linearly associated with MJO from those that are not to analyze the 96 events following MJO phase 3. We note that regressed fields are similar between the events with and without ARs due to the fixed-MJO-phase approach.

a. All MJO events

As described in section 2b, extratropical fields are decomposed into MJO regressed and residual components using linear regression onto the RMM indices. Figure 7 shows the 96 event composites of regressed and residual fields for 500-hPa geopotential height and 250-hPa meridional wind fields. Since the MJO is an intraseasonal tropical oscillation, the MJO linearly dependent component is characterized by a statistically significant slow-moving positive height pattern over the North Pacific (Fig. 7a). The upper-tropospheric wind is nearly geostrophic, where the southerly wind over the northwest of the ridge supports vapor flux from East Asia (see also Fig. 8a). The residual composite reveals synoptic-scale waves over the Pacific storm track (Fig. 7b) that propagate downstream with a clear wave packet structure at days 6–8 (zonal wavenumber of about 5–7). This indicates that ubiquitous synoptic-scale baroclinic waves have phases linearly unrelated to the MJO. The signal amplitude is similar for both composites with a maximum height perturbation of about 45 m.

Fig. 7.
Fig. 7.

Composites of (a) MJO regressed and (b) residual 500-hPa geopotential height anomaly (shading; m) and 250-hPa meridional wind anomaly (contours; interval of 1 m s−1) for the 96 MJO events at lags 0–10 days. Color shading in (a) is only shown where height anomalies are statistically significant considering the false discovery rate. Gray and black stippling in (b) denote where height anomalies are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for IVT anomaly (kg m−1 s−1).

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

Figure 8 shows the regressed and residual IVT composites. The MJO linearly related response in IVT is also a slow-moving planetary-scale feature (Fig. 8a) consisting of three zonally oriented bands: tropical moisture transport, suppressed moisture transport over the mid-Pacific, and the large-scale water vapor transport extending from East Asia across the Pacific, consistent with various AR activity composites during MJO phases 3–4 [see Fig. 11 of Guan and Waliser (2015), Fig. 10 of Mundhenk et al. (2016a), and Fig. 2b of Zhou et al. (2021)]. The regressed field becomes weaker at longer leads because individual MJO events propagate differently (i.e., MJO phases become inconsistent among the events). In line with the geopotential height results, the residual IVT field also exhibits a synoptic-scale wave structure with a faster propagation speed than the dependent component (Fig. 8b). On day 0, a positive anomalous vapor transport region appears at the leading edge of the large-scale moisture transport found in the dependent component. The positive and negative IVT anomaly regions subsequently propagate downstream in the following days, consistent with the baroclinic waves evident in the wind field. The residual field has little significant signal, reflecting the large variability among cases.

b. MJO events with and without ARs

Now we partition the regressed and residual extratropical states between MJO events with and without ARs. Because there is a significant low-frequency difference between the two groups, as shown in Fig. 5, we further decompose the geopotential height field into high-frequency (< 10 days) and low-frequency (>10 days) components using a Lanczos filter with 121 weights. Figure 9 shows the composites of individual components for the two groups on the first day of MJO phase 3 (lag 0). The linear extratropical response to MJO is overall similar between the two groups due to the conditioning on MJO phase 3, and almost entirely low frequency because we regressed fields onto the RMM indices that have a dominant portion of the variance in the intraseasonal time scale. The relatively larger amplitude for the MJO events without ARs is due to the difference in predictor component amplitudes (Fig. 4a). While the MJO could alter high-frequency waves through nonlinear interactions with the extratropical circulation (e.g., Wang et al. 2018), this effect is not considered in this study. The ridge over the central Pacific in the regressed low-frequency component slowly propagates eastward, similar to the pattern in Fig. 7a.

Fig. 9.
Fig. 9.

Composites of (a) MJO regressed and (b) residual geopotential height anomaly (shading; m) at 500 hPa for MJO events (left) with ARs and (right) without ARs at lag 0 days. The geopotential height field is partitioned into 10-day high-pass (<10 days) and 10-day low-pass (>10 days) fields. Lines in (b) are drawn by connecting the local extrema in the high-pass geopotential height anomaly. Color shading in (a) indicates statistical significance considering the false discovery rate. Gray and black stippling in (b) denote where anomalies/differences are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

In contrast, both low- and high-frequency signals are evident in the residual field (Figs. 9b). The bottom panels of Fig. 9b reveal a contrasting height pattern over the Gulf of Alaska: troughs for the cases with ARs and ridges for the cases without ARs. This low-frequency pattern for the events with ARs resembles the height pattern 6–12 days prior to AR landfalls over Oregon (Benedict et al. 2019), where a positive geopotential height anomaly is located over Alaska/Bering Strait with a negative height anomaly to the south. Due to this low-frequency pattern, the large-scale ridge in the dependent component extends or retracts to form the height structure shown in Fig. 5. The ray paths of baroclinic waves (lines connecting the local extrema in the top panels of Fig. 9b) are convex equatorward for the cases with ARs and convex poleward for the cases without ARs, which reflects changes in the waveguide associated with the low-frequency extratropical variability in the residual field. A Student’s t test shows that the wave packets are significant with 95% confidence for the events with ARs. Although the region of significance is smaller in the sample without ARs, the difference between AR and without AR samples is statistically significant in both low- and high-frequency patterns (not shown). Furthermore, similar amplitudes are found in regressed and residual low-frequency geopotential height anomalies (bottom panels of Figs. 9a and 9b). Although this result suggests roughly equal contributions from the two components, we recognize that other factors not considered in our linear regression framework, such as differences in static stability, can also influence AR events along the west coast of North America.

The identified low-frequency extratropical pattern in the residual field (bottom panels of Fig. 9b) resembles the PNA pattern (Wallace and Gutzler 1981), with height anomalies centered around the Gulf of Alaska and northwest Canada. This result is consistent with the finding of Toride and Hakim (2021), who showed the importance of low-frequency PNA variability in conjunction with the MJO in controlling AR activity. They also showed that conditionally sampling the MJO on monthly values for the PNA index is equivalent to linearly superimposing monthly PNA anomalies on MJO teleconnection patterns, consistent with our results.

The regressed IVT is similar to Fig. 8a for both cases, reflecting large-scale moisture transport into developing ARs, which mostly form and propagate in the residuals (Fig. 10). Moreover, although synoptic-scale waves are found in both samples (with and without ARs), the ray paths of the wave packet differ, such that moisture is transported to the Pacific Northwest in the group with ARs, and shifted northward in the group without ARs. The low-frequency extratropical trough–ridge pattern centered over the Gulf of Alaska is responsible for the orientation of these waves.

Fig. 10.
Fig. 10.

Composites of residual IVT anomaly (kg m−1 s−1) for MJO events (a)–(c) with ARs and (d)–(f) without ARs at lags of (top) 0, (middle) 5, and (bottom) 10 days. Gray and black stippling denote where anomalies are statistically significant at the local significance level of 0.05 and after controlling for the false discovery rate, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

5. Conclusions

In this study, we analyze 96 MJO events following MJO phase 3 (convection over the Indian Ocean) to diagnose the differences in tropical and extratropical configuration between MJO events that are followed by ARs and those that are not. Although a simple lag composite of MJO events shows an increase in AR activity averaged over the Pacific Northwest coastal region (Fig. 3c), ARs do not affect the Pacific Northwest in nearly half of the cases (Fig. 2a). We find a longer duration for MJO phase 3 in the cases with ARs (Fig. 4) and also a significant phase difference in midlatitude trough and ridge locations over the North Pacific between these MJO events with and without ARs (Fig. 5). In particular, large-scale anticyclonic flow develops and prevents moisture access to the Pacific Northwest in the MJO events without ARs, while large-scale cyclonic flow enhances vapor flux to the Pacific Northwest in the MJO events with ARs (Fig. 6).

By regressing out the RMM indices, we identify a contrasting extratropical signal between the MJO events with and without ARs. This extratropical signal has low-frequency variability (>10 days) and maximum amplitude over the Gulf of Alaska (bottom panels of Fig. 9b). The linear extratropical response to MJO events following phase 3 is a ridge over the central Pacific that supports large-scale water vapor transport across the North Pacific (Figs. 7a and 8a), whereas the residual extratropical pattern affects the midlatitude baroclinic waveguide (Fig. 7b). When the extratropical variability produces troughs in the Gulf of Alaska (i.e., positive PNA pattern), baroclinic waves are directed into the west coast with an orientation that favors enhanced vapor flux and ARs (Fig. 9b). The linear MJO response appears to contribute to AR events primarily through increasing large-scale water vapor transport across the North Pacific, which is then available for these extratropical events (Fig. 8a). We further find that the regressed and residual geopotential height patterns have nearly equal amplitude over the North Pacific (bottom panels of Figs. 9a and 9b).

This study highlights the importance of low-frequency extratropical variability for AR events in the Pacific Northwest. Although the extratropical patterns identified in this study are linearly uncorrelated with the MJO, the PNA pattern is known to be associated with waves radiating from anomalous tropical heating (Hoskins and Karoly 1981), extratropical high-frequency eddies (Lau 1988; Branstator 1992), and eddy growth by extracting kinetic energy from zonally varying background flow (Simmons et al. 1983; Mori and Watanabe 2008). Whether the identified pattern is connected to tropical heating through nonlinear processes, or is indeed primarily internal extratropical variability, remains an open question. Future work may further expand these concepts to improve the subseasonal AR forecasts based on both tropical and extratropical dynamics.

Acknowledgments.

KT was supported by JSPS KAKENHI Grant 19J01337, 20K14833, and the University of Washington. GH was supported by NOAA Grant NA20NWS4680053.

Data availability statement.

The MERRA-2 dataset is openly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/). The ONI index is available from the National Oceanic and Atmospheric Administration/Climate Prediction Center (https://psl.noaa.gov/data/correlation/oni.data). The RMM index is available from the Australian Bureau of Meteorology (http://www.bom.gov.au/climate/mjo/).

APPENDIX A

AR Detection Method

To analyze the difference between AR detection methods based on regional-averaged and gridpoint IVT anomalies, quantiles of IVT are compared in Fig. A1 for the 96 MJO events considered here. The maximum IVT during lags 3–19 days following MJO phase 3 is used to estimate the quantiles for the regional-averaged method. The MJO events that exceed the 95th percentile of regional-averaged IVT are considered MJO events with ARs in this study. For the grid point approach, the maximum quantile during lags 3–19 days is estimated at each of the 10 yellow grid points in Fig. 1. Then, the average of the top three quantiles is used as the y axis in Fig. A1, representing ARs that partially affect the target region. The quantiles of IVT are calculated based on IVT anomalies (either regionally averaged or by grid point) in NDJFMA during 1980–2019. Overall, the results show a consistent tendency between the two methods with higher quantile scores for the grid-point based approach. The 95th percentile of regional-averaged IVT roughly corresponds to the 98th percentile of gridded IVT. The main conclusions of this study do not depend upon this choice.

Fig. A1.
Fig. A1.

Scatterplots of quantiles of maximum regional-averaged IVT during lags 3–19 days (x axis) and quantiles of gridded IVT during lags 3–19 days for the 96 MJO events (y axis). Quantiles of gridded IVT are averaged over the top three grid points. The vertical and horizontal lines correspond to 0.95 and 0.98 quantiles, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

APPENDIX B

Seasonality in MJO and AR

For reference, we include the seasonality of MJO and AR events detected in this study (Fig. B1). About 80 AR events are detected each month from November to January, whereas the number of detected cases drops by nearly half from February to April. The number of MJO events has a decreasing trend from November to April, except for March when 20 MJO events are found.

Fig. B1.
Fig. B1.

Number of MJO events (gray; left axis) and AR events (blue; right axis) as a function of month.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0493.1

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