The Maritime Continent Barrier Effect on the MJO Teleconnections during the Boreal Winter Seasons in the Northern Hemisphere

Yihao Zhou aKey Laboratory of Mesoscale Severe Weather (MOE), School of Atmospheric Sciences, Nanjing University, Nanjing, China

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Shuguang Wang aKey Laboratory of Mesoscale Severe Weather (MOE), School of Atmospheric Sciences, Nanjing University, Nanjing, China

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Juan Fang aKey Laboratory of Mesoscale Severe Weather (MOE), School of Atmospheric Sciences, Nanjing University, Nanjing, China

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Da Yang bCollege of Agricultural and Environmental Sciences, University of California Davis, Davis, California
cEarth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, California

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Abstract

The Maritime Continent disrupts eastward propagation of the Madden–Julian oscillation (MJO). This study surveys the impact of the disruption—often known as the barrier effect—on the MJO teleconnections. The MJO propagation may be broadly categorized based on whether the MJO precipitation crosses the Maritime Continent (MC) during extended boreal winter seasons: successfully propagating across the MC (MJO-C) or being blocked by the MC (MJO-B). Compositing atmospheric circulation upon these two categories reveals that precipitation anomalies of MJO-C are stronger and more coherent than those of MJO-B, while their phase speed and lifetime are comparable. MJO-C and MJO-B excite distinct extratropical responses due to their diabatic heating in the deep tropics. Midlatitude circulation displays stronger and long-lasting negative geopotential anomalies in the northern Pacific Ocean 5–14 days after phase 7–8 of MJO-C, but significantly weaker anomalies from MJO-B. The extratropical water vapor transport during MJO-B and MJO-C differs markedly after phase 2. The Pacific–North American (PNA) pattern and North Atlantic Oscillation (NAO) both show significant response after phase 6 of MJO-C as its precipitation anomaly over the tropical Pacific during this period is stronger, while MJO-B has little impact on both. Surface air temperatures (SAT) at high latitudes during MJO-B and MJO-C are also significantly different. SAT is weaker and delayed in MJO-B in comparison to MJO-C, likely due to different meridional eddy heat fluxes.

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

This article is included in the Years of the Maritime Continent Special Collection.

Corresponding author: Shuguang Wang, wangsg@outlook.com

Abstract

The Maritime Continent disrupts eastward propagation of the Madden–Julian oscillation (MJO). This study surveys the impact of the disruption—often known as the barrier effect—on the MJO teleconnections. The MJO propagation may be broadly categorized based on whether the MJO precipitation crosses the Maritime Continent (MC) during extended boreal winter seasons: successfully propagating across the MC (MJO-C) or being blocked by the MC (MJO-B). Compositing atmospheric circulation upon these two categories reveals that precipitation anomalies of MJO-C are stronger and more coherent than those of MJO-B, while their phase speed and lifetime are comparable. MJO-C and MJO-B excite distinct extratropical responses due to their diabatic heating in the deep tropics. Midlatitude circulation displays stronger and long-lasting negative geopotential anomalies in the northern Pacific Ocean 5–14 days after phase 7–8 of MJO-C, but significantly weaker anomalies from MJO-B. The extratropical water vapor transport during MJO-B and MJO-C differs markedly after phase 2. The Pacific–North American (PNA) pattern and North Atlantic Oscillation (NAO) both show significant response after phase 6 of MJO-C as its precipitation anomaly over the tropical Pacific during this period is stronger, while MJO-B has little impact on both. Surface air temperatures (SAT) at high latitudes during MJO-B and MJO-C are also significantly different. SAT is weaker and delayed in MJO-B in comparison to MJO-C, likely due to different meridional eddy heat fluxes.

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

This article is included in the Years of the Maritime Continent Special Collection.

Corresponding author: Shuguang Wang, wangsg@outlook.com

1. Introduction

The Madden–Julian oscillation (MJO) is the dominant planetary-scale convective structure and intraseasonal mode in the tropics (Zhang et al. 2020). The MJO generally propagates eastward from the Indian Ocean to the central Pacific Ocean with a speed of ∼5 m s−1, recurring at Earth’s equator every 30–90 days (Xie et al. 1963; Madden and Julian 1971, 1972; Li et al. 2018). The anomalous convection of the MJO as a diabatic heating source excites poleward-propagating Rossby wave trains, and modulates weather and the general circulation at the mid- and high latitudes (Matthews et al. 2004; Zhang 2005; Seo and Son 2012).

Many authors have investigated the impact of the MJO on the extratropical weather from both observation and numerical simulations. The intraseasonal geopotential height anomaly in the midlatitudes can be significantly modulated by the MJO-induced heating in the tropics (e.g., Hoskins and Sardeshmukh 1987; Matthews et al. 2004; Gloeckler and Roundy 2013). The MJO modulates the occurrence of the midlatitude blocking events and extratropical cyclones (Henderson et al. 2016), potentially leading to extreme temperature and precipitation events. The MJO also interacts with some important climate modes, for example, the Pacific–North American (PNA) pattern and North Atlantic Oscillation (NAO). The positive (negative) phase of PNA is most likely to take place about 5–10 days after MJO phase 7 (3) when the enhanced (suppressed) convection is located over the tropical western Pacific (e.g., Mori and Watanabe 2008; Tseng et al. 2018). The positive (negative) phase of NAO tends to occur after MJO phase 3 (7) with about 10–15 lag days (Cassou 2008; Lin et al. 2009). Recently, Tseng et al. (2021) further showed that the PNA pattern is one of the leading modes over the extratropical Pacific driven by tropical convection, and its prediction skill reaches more than two weeks. Lee et al. (2019) found that El Niño–Southern Oscillation (ENSO) could influence the Rossby wave generation and propagation direction associated with the MJO by modulating the zonal scale of dipolar tropical heating, and thus modify the MJO–NAO teleconnection. The atmospheric rivers (ARs) are often associated with the extratropical vapor transport and have a profound impact on extreme precipitation events in the midlatitudes (e.g., Zhu and Newell 1998; Gimeno et al. 2014). The ARs can be modulated by the anomalous extratropical circulation excited by the MJO. At high latitudes, surface air temperature can also be influenced by the MJO convection through dynamical processes, such as eddy heat flux associated with the poleward-propagating Rossby wave trains from the MJO (e.g., Lee et al. 2011; Yoo et al. 2012; Hu et al. 2019).

The spatial pattern and amplitude of the tropical heating vary among individual MJO events. As a result, the extratropical Rossby wave responses differ markedly. Yadav and Straus (2017) showed that the response over North Atlantic to slow‐propagating MJO is much stronger than that of fast‐propagating events. Several authors used idealized models to test the sensitivity of the extratropical response to the different tropical forcing associated with the MJO. Goss and Feldstein (2018) suggested that the response is sensitive to the amplitude of the tropical heating anomalies, while the propagation speed of MJO has little impact. Zheng and Chang (2019) found that intensity, propagation speed, and timing of initiation and decay of the MJO can all play important roles on the extratropical responses. In addition to the Rossby wave source, the midlatitude westerly jet as the waveguide for Rossby wave propagation is also considered critical to the MJO heating excited extratropical responses (e.g., Sardeshmukh and Hoskins 1988; Jin and Hoskins 1995; Bao and Hartmann 2014; Henderson et al. 2017; Wang et al. 2020; Zheng and Chang 2020).

These studies suggest that the spatial and temporal distribution of the MJO heating is crucial for understanding its extratropical impact. The MJO convection often weakens or even stalls during its passage from the Indian Ocean to the Maritime Continent (MC), a phenomenon known as the “barrier effect” (Hendon and Salby 1994; Hsu and Lee 2005; Inness et al. 2003; Inness and Slingo 2006; Ling et al. 2019; Rui and Wang 1990; Zhang and Ling 2017). Some MJO events can overcome the barrier effect and enter the western Pacific, but others would weaken in the western Pacific or completely stall near the MC. Zhang and Ling (2017) refer to them as MJO-C and MJO-B, respectively. The propagation, amplitude, and distribution of the anomalous tropical heating may significantly differ between these two types of MJOs. The barrier effect significantly limits the MJO prediction skill in nearly all numerical weather prediction models (Wang et al. 2019). Given the large difference in the MJO convection due to the MC barrier effect, it is reasonable to expect that the barrier effect can also cause extratropical responses to the MJO to diverge.

Our hypothesis is that the MC barrier effect on the MJO would cause substantial differences in the extratropical responses. We will test this hypothesis using observation datasets. Specifically, we will explore the impacts of the MJO events on the extratropical weather based on the MC barrier effect—namely, whether or not the MJO convection can propagate across the MC. Our focus is the Northern Hemisphere during the extended boreal winter MJO seasons (November–April), when the MJO teleconnection patterns are most significant in the Northern Hemisphere due to the strong extratropical jet, which can provide a favorable waveguide for Rossby wave propagation (e.g., Weickmann 1983; Weickmann et al. 1985; Hsu 1996; Jin and Hoskins 1995).

The rest of the article is structured as follows. In section 2, we introduce datasets and the methodology. Section 3 shows the propagation characteristics of MJO-B and MJO-C and their precipitation distribution. Section 4 discusses how MJO-B and MJO-C influence extratropical weather systems through diabatic heating–induced Rossby wave trains. We will focus on the anomalous geopotential height, water vapor transport, PNA, NAO, and high-latitude surface air temperature from MJO-B and MJO-C, respectively. Section 5 summarizes the results with a schematic diagram.

2. Data and methods

The Maritime Continent barrier on the MJO propagation is quantified using the precipitation-based index (PII; Wang 2020), which is suitable for both the boreal winter and summer seasons as its EOFs are obtained using precipitation anomalies that vary with the season. We used this index to quantify the MJO propagation because it is based on the precipitation that induces tropical heating and excites the extratropical responses, and also because the MC barrier effect is better defined based on the MJO precipitation (Zhang and Ling 2017). Another widely used MJO index is the OLR-based MJO index (OMI; Kiladis et al. 2014; Wang et al. 2018). PII is related to OMI, but focuses on precipitation. The maximum bivariate correlation coefficient between PII and OMI is ∼0.8 during the 2000 and 2014 periods (see Table 1 in Wang 2020), as they both reflect the MJO convection to some degree. Our tests indicate that the overall results and conclusions are robust whether PII or OMI is used, but the contrast between MJO-C and MJO-B is larger using PII. In addition, there is a phase lag between PII and OMI. The phase of PII leads that of OMI by a few days during the boreal winter season (see Table 1 in Wang 2020); that is, the Nth phase composite from PII approximately corresponds to the (N − 1)th phase from OMI. We will categorize the MJO based on whether or not the MJO precipitation crosses the MC, and refer to them as MJO-C (MJO crossing) and MJO-B (MJO blocking), following the notation in Zhang and Ling (2017). Specifically, MJO-C refers to the MJO events that can successfully propagate from Indian Ocean to the western Pacific Ocean while maintaining its amplitude measured using the PII. MJO-B refers to the MJO events whose convection is significantly inhibited or stalled over the MC region and cannot reach western Pacific. To compute the MJO index, we use the precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 product (Huffman et al. 2007) to first obtain the sliding-window EOFs of eastward propagating equatorial precipitation anomalies (20°S–20°N); and the PII is computed by projecting the precipitation anomalies onto the two EOFs. Because this TRMM dataset is available from 1998, we use the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR; hereafter P-CDR) (Hsu et al. 1997) daily precipitation data with 0.25° resolution from 1983 to 2019. Precipitation anomalies from P-CDR is then projected onto the same PII-EOFs to obtain longer time series of the PII.

We interpolate the daily P-CDR precipitation data into a new grid at 2.5° resolution, and then process it in the following steps (Wang 2020): 1) remove the time average and first three harmonics of the annual cycle for the 1983–2019 period and 2) apply a 20–96-day bandpass filter to precipitation anomalies from step 1 with a 139-weight Lanczos filter. To calculate the PII based on P-CDR, we then project the resultant equatorial precipitation anomalies (20°S–20°N) of P-CDR onto the two leading rotated EOFs from TRMM precipitation, and normalize these two time series by their respective standard deviation. The correlation coefficient between the two PIIs based on TRMM and P-CDR is 0.98 for both components during the period from 1998 to 2018. Figure 1 shows the similar probability density distributions of these two PIIs, which approximately follows the standard normal distribution. The comparison suggests that this extended PII (1983–2019) from P-CDR may be used to quantify the phase and amplitude of MJO precipitation appropriately. We will use it to identify whether an MJO event is MJO-B or MJO-C.

Fig. 1.
Fig. 1.

Probability density distributions of the two components of PII based on P-CDR (solid) and TRMM (dashed) during the 1998–2018 period.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

We focus on the MJO events that initiate over the Indian Ocean and propagate eastward (i.e., the phase number does not decrease with time) to ensure that the tracked days in each successive phase are part of the same MJO events. Based on these successive days in each period, the MJO events are identified if the averaged PII amplitude from phase 2 to 3 is greater than 0.8. We used this threshold instead of 1 as some studies (e.g., Barrett et al. 2021) to have more MJO events for composite analysis, and the overall results in this study are not sensitive to this choice (see Text S1 and Figs. S1–13 in the online supplemental material). We have also calculated the mean (∼1.28) and standard deviation (∼0.73) of the PII amplitude in the boreal winter. Because the amplitude threshold values of 0.8 and 1 are both greater than the mean minus one standard deviation, and because the propagation characteristics of the MJO events with amplitude between 0.8 and 1 are similar to those greater than 1, we consider that using the threshold value 0.8 is justified. A total of 87 MJO events are identified during the extended boreal winter (November–April) from 1983 to 2019. For each MJO event, we then use the averaged amplitude of the PII from phases 6 to 8 (the MJO has traveled through MC and reached the western Pacific during this period) to composite the MJO events: if the amplitude is less than 1.5 it is considered as MJO-B, and if greater than 1.5 as MJO-C. The classification threshold of 1.5 leads to half of the MJO events blocked by the MC, and the overall results in this study are still robust if we change this value slightly (±0.2). The precipitation composites (as discussed below for Figs. 2 and 3) indicate that the 1.5 threshold value is appropriate for separating the MJO into these two groups. While the threshold value may be somewhat subjective, it is consistent with some past studies; for example, Vitart and Molteni (2010) found that 30% of MJOs cannot cross the MC in the reanalysis dataset. Kerns and Chen (2016) showed 43% of non-MC-crossing MJOs by tracking the propagation of MJO precipitation. Zhang and Ling (2017) identified the MJO-C and MJO-B events, and found that about half of the total MJO events are blocked by the MC in boreal winter using a different precipitation-tracking method.

Fig. 2.
Fig. 2.

Composite of the PII for MJO-B (blue) and MJO-C (red) averaged from individual events. Each phase is equally divided into three regions with an interval of π/12. Large-size dots indicate the starting points in phase 1. The approximate location of the MJO convection is shown in parentheses for each phase. SPCZ is the South Pacific convergence zone; IO is the Indian Ocean; MC is the Maritime Continent; WP is the western Pacific.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

Fig. 3.
Fig. 3.

Composite time–longitude diagrams averaged over (top) 15°S–15°N, (middle) 15°S–0°, and (bottom) 0°–15°N for precipitation anomalies of (a)–(c) MJO-B and (d)–(f) MJO-C. Lag 0 is the first day in phase 5. Results significant at the 95% confidence level are dotted.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

A few MJO events that propagate westward (∼5 cases; e.g., propagating from phase 5 to 4) or skip phases (1 case; e.g., propagating from phase 4 to 6 and skipping phase 5) over the MC during phase 4 or 5 are identified as MJO-B events, as these unusual propagation features almost always have small amplitude of PII, indicating they are blocked by the MC barrier. There are also a few events (∼7 cases) that propagate westward or skip phases after phase 5, and only the days before this phase are counted and the amplitude in any subsequent phase(s) is excluded in the calculation of the averaged amplitude during phases 6–8. These westward or skipping propagating events are uncommon, likely due to noise. We have repeated our analysis using eastward-only precipitation anomalies, and the results are also robust to these small number of events.

A total of 43 MJO-B events and 44 MJO-C events are identified from total 87 MJO events. Some of these MJO-B events (∼13 cases) are very weak during phases 6–8 (smaller than 0.8), and these days may not be strictly considered as significant after passing through the MC as our purpose is to compare the MJO crossing or blocking by the MC. To increase the sample size during phases 6–8 of MJO-B, however, we will not exclude these days in the following composite analysis. The Student’s t test is used to assess whether the composite results are statistically significant at the 95% confidence level. The degree of freedom is the number of the MJO events: 43 for MJO-B and 44 for MJO-C.

The fifth generation of the European Centre for Medium‐Range Weather Forecasts (ECMWF) atmospheric reanalysis data (ERA5; Hersbach et al. 2020), including wind, temperature, geopotential height, and specific humidity, are used to study the tropical features and extratropical impacts of MJO-B and MJO-C. The reanalysis data are averaged on a grid at 2° horizontal resolution. The climatology of these variables is also removed by subtracting the time mean and the first three harmonics of annual cycle to extract the anomalous signals.

3. Tropical features of MJO-B and MJO-C

a. Composite of the PII

Figure 2 shows the composite of PII in the eight phases of MJO-B and MJO-C events. We divide each phase equally into three bins (each bin with phase angle increment of π/12) and composite the corresponding PII values in each bin for both MJO-B and MJO-C, respectively. As shown in Fig. 2, their amplitudes are similar in phases 1 and 2, but the amplitude of MJO-C grows larger after phase 3. Their difference reaches a maximum during phases 7 and 8 over western Pacific, and the amplitude of MJO-C is about twice larger than MJO-B. This result indicates that the precipitation of MJO-B becomes weaker after passing through the MC, while that of MJO-C remains nearly unchanged during the propagation.

b. Precipitation and 850-hPa wind

The composite time–longitude diagrams of tropical (15°S–15°N) precipitation anomalies from MJO-B and MJO-C are shown in Figs. 3a and 3d. The precipitation anomalies associated with MJO-B and MJO-C both initiate over the Indian Ocean near 60°E and propagate eastward at similar speed, but the amplitude of MJO-C is significantly stronger after lag −10 (∼phase 3). Importantly, both the MJO-B and MJO-C cases display slow eastward propagation on the intraseasonal time scale; hence they are considered as the MJO. The positive precipitation anomalies of MJO-B and MJO-C decrease when they enter the MC near 100°E, recover some strength near 120°E, and continue to propagate eastward. The MJO-C precipitation anomaly reaching the western Pacific (east of 150°E) is stronger (∼3 mm day−1), while the MJO-B precipitation almost vanishes (<1 mm day−1). Figures 3b, 3c, 3e, and 3f show that MJO-B and MJO-C precipitation signals are both stronger and more continuous in the Southern Hemisphere than the Northern Hemisphere, consistent with the typical distribution of boreal winter MJO precipitation over MC (Hendon and Salby 1994; Zhang and Dong 2004; Kim et al. 2017; Zhang and Ling 2017). In general, MJO-C has stronger positive and negative precipitation anomalies (especially the stronger positive precipitation signal over western Pacific) and less disruption by the MC in both hemispheres.

Figure 4 displays the spatial evolutions of precipitation and 850-hPa wind anomalies of MJO-B and MJO-C. Their positive precipitation anomalies initiate over the Indian Ocean in phase 2, and the amplitude of MJO-C is slightly stronger. During phases 3 and 4, the active convection of MJO-C is well developed over the eastern Indian Ocean with the precipitation anomaly reaching ∼9 mm day−1, accompanied by the enhanced westerly anomalies. On the other hand, the precipitation anomaly from MJO-B is much weaker (∼3 mm day−1), and the westerly anomaly is about 2 or 3 times weaker than MJO-C in that region. In the subtropics, there are also positive precipitation anomalies (∼3 mm day−1) over East Asia and the northwestern Pacific region (∼30°N) in MJO-C (Figs. 4j–l), consistent with the results in previous studies (Jeong et al. 2008; Jia et al. 2011; Chen et al. 2021), while the MJO-B signal is very weak or insignificant. When they reach the MC in phases 5 and 6, the precipitation and westerly anomalies of MJO-C are also much stronger than those of MJO-B, especially over the oceans immediate south of the equator (the Java Sea, Banda Sea, Timor Sea, and Arafura Sea), which constitute the main pathway by which the MJO crosses the MC. Precipitation anomalies on both islands and oceans associated with MJO-C are stronger than those with MJO-B during this period, but their maximum difference occurs on the oceans and reaches 2 mm day−1 for regional averages, similar to Fig. 12 in Zhang and Ling (2017). During phases 7 and 8, the positive precipitation anomalies of MJO-C are located south of equator and are stronger (∼9 mm day−1), while those of MJO-B are much weaker (∼3 mm day−1). The zonal scale of the tropical westerly of MJO-C is broader (from 100°E to 180°) than MJO-B, and there lacks a significant westerly center of MJO-B near the equator. The MJO-B positive precipitation anomaly over the Pacific is about 2 mm day−1 stronger than that over the Indian Ocean during phases 1 and 2, similar to the phase composite based on the PII (Fig. 8 in Wang 2020). However, the precipitation anomaly over the Pacific of MJO-C during this period is relatively weaker.

Fig. 4.
Fig. 4.

Composites of precipitation (color shading; mm day−1) and 850-hPa wind (vectors; m s−1) anomalies in the eight phases of (left) MJO-B and (right) MJO-C. Results statistically significant at the 95% confidence level for precipitation and wind are dotted and presented as thick black vectors, respectively.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

4. Extratropical impacts of MJO-B and MJO-C

a. 500-hPa geopotential height and Rossby wave source

Figures 5 and 6 show the anomalous 500-hPa geopotential height (Z500) at different phases in the Northern Hemisphere during lag days 5–9 and 10–14, respectively, as the extratropical response to tropical forcing is typically established in about two weeks (e.g., Stan et al. 2017). The spatial pattern of Z500 is characterized by the extratropical wave trains extending from central Asia to eastern North America. The strongest positive response over the North Pacific occurs 5–9 days after phases 3–4 (Figs. 5c,d,k,l) when enhanced MJO heating (cooling) is located in the Indian (Pacific) Ocean, as found in the past studies (e.g., Lin et al. 2010; Lin and Brunet 2018; Tseng et al. 2019). The spatial patterns and temporal evolution of the extratropical responses from MJO-B and MJO-C are broadly similar in some regions (e.g., North Pacific), but there are also significant differences in amplitude and spatial distribution. In phase 4, the maximum amplitude of positive Z500 anomalies over the North Pacific of MJO-B is stronger than MJO-C (Figs. 5d,l), and the MJO-B response lasts longer (up to lag 14; Fig. 6d). Their spatial pattern also differs significantly (e.g., their meridional tilting directions are opposite). The response near Alaska is weak for MJO-B, while the Z500 anomalies of MJO-C are significantly negative there around phase 4. During phases 7 and 8, the negative Z500 anomaly over the North Pacific of MJO-C is about twice as strong and longer lasting (up to lag 14 days; Figs. 6o,p), while the MJO-B response almost completely disappears at lag days 5–9 (Fig. 5h). This distinction might be attributed to the stronger extratropical waves from MJO-C excited by the anomalous heating over the western Pacific, while the forcing of MJO-B during phases 7 and 8 is very weak. The Z500 response in central Asia from MJO-C is also twice as strong. The signs of response near Alaska from MJO-B and MJO-C are opposite at lag days 5–9 during phases 7 and 8 (Figs. 5g,h,o,p).

Fig. 5.
Fig. 5.

Composites of anomalous 500-hPa geopotential height (m) for lag 5–9 days after each phase of (left) MJO-B and (right) MJO-C. Results significant at the 95% confidence level are dotted.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for lag 10–14 days.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

In general, the Z500 spatial pattern in the Northern Hemisphere from MJO-C from the PII is similar to that obtained from other MJO indices composited upon the strong MJO days when their amplitudes are greater than 1 in many previous studies (e.g., Tseng et al. 2018). This consistency implies that the composite results based on PII can be reasonably compared with other studies focusing on the MJO teleconnections using the RMM (Wheeler and Hendon 2004) or OMI (Kiladis et al. 2014). They all have a quadrupole wave train structure with four centers in East Asia, the North Pacific, Alaska, and eastern North America during certain phases. However, this spatial pattern is barely detectable in MJO-B, and its anomalies over the North Pacific show an unusual southwest–northeast tilted feature. These results imply that there are likely other factors modulating the responses besides the amplitude of MJO-related tropical heating; for example, the spatial structure of MJO, the propagation characteristics (especially that near the MC), or other weather systems, such as the intrinsic extratropical subseasonal to seasonal variability (Stan and Krishnamurthy 2019) can also modulate the MJO extratropical responses (Zheng and Chang 2020). However, the detailed mechanism is beyond the scope of this study, which requires further investigation in the future.

We examine the Rossby wave source (RWS) to diagnose the Rossby wave emission associated with the MJO. The RWS is an important indicator of wave emission in regions with vigorous convection, and thus it can be well represented by the MJO diabatic forcing signal. The RWS may be defined as (Sardeshmukh and Hoskins 1988)
RWS=ζVχ=ζ¯VχVχζ¯ζV¯χV¯χζ,
where ζ denotes the absolute vorticity, Vχ is the irrotational wind, and overbars and primes indicate climatology and anomalies, respectively. The first (third) term on the right side denotes the stretching of climatological (anomalous) absolute vorticity by the anomalous (climatological) divergence, and the second (fourth) term indicates the advection of climatological (anomalous) absolute vorticity by the anomalous (climatological) irrotational wind. The climatological values (V¯χ and ζ¯) at each date of the year are mean and the first three harmonics of the multiyear averages during the 1983–2019 period. The anomalous values (Vχ and ζ) during the 1983–2019 period are departures from the respective climatologies.

Figure 7 shows the 200-hPa RWS and irrotational flow associated with MJO-B and MJO-C averaged during lag days 0–4 for individual phases. The anomalous flow is relatively weak in phases 1 and 2 for both MJO-B and MJO-C (Figs. 7a,b,i,j), since the tropical precipitation anomalies during this period are suppressed. The increased precipitation over eastern Indian Ocean of MJO-C during phases 3 and 4 leads to the enhanced upper-level divergence over the jet entrance region, which contributes to a strong negative RWS over the Asia-Pacific region mainly through the stretching by the anomalous divergence. However, the Rossby waves excited by MJO-B are weak during this period since the tropical precipitation anomalies over Indian Ocean are weak. This remarkable distinction in RWS between MJO-B and MJO-C in the eastern Indian Ocean and East Asia might have significantly different impacts on the evolution of Z500 over the North Pacific (e.g., the strong negative RWS of MJO-C in East Asia enhances the eastward propagation of the negative Z500 anomalies in central Asia, which suppresses the positive Z500 anomalies over the North Pacific and leads to its early decay). It is likely that Rossby waveguide along the jet stream may also contribute to the differing impacts of the MJO-B and MJO-C. However, the mechanism is beyond the scope of this study.

Fig. 7.
Fig. 7.

Composites of 200-hPa linear Rossby wave source (color shading; ×10−10 s−2) and anomalous 200-hPa irrotational wind (vectors; m s−1) for lag 0–4 days after each phase for (left) MJO-B and (right) MJO-C. Results significant at the 95% confidence level for Rossby wave source and wind are dotted and presented by thick black vectors, respectively; vectors with amplitude smaller than 0.8 m s−1 are omitted.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

The extratropical RWS of MJO-C grows stronger around phases 4 and 5, and its negative anomalies expand to the central North Pacific accompanied by the anomalous divergent wind in high latitudes. As the MJO tropical precipitation moves eastward across MC, RWS then decays in phase 6. In phase 7, the anomalous convergence associated with positive RWS gradually develops over the jet entrance region, and reaches a maximum in phase 8 when the active tropical convection is located over western Pacific. In general, the RWS associated with MJO-C is characterized by strong anomalous irrotational flow along the jet and a dipolar pattern around 140°E during phase 3 and 7, similar to Fig. 7 in Tseng et al. (2019). In contrast, the MJO-B shows weak RWS and irrotational wind in all these stages.

To further investigate contributions from each term in the RWS budget equation, averaged values near the jet region (25°–45°N, 90°–160°E) are shown in Fig. 8. The first term (ζ¯Vχ, the stretching of climatological absolute vorticity by the anomalous divergence) is the largest for both MJO-B and MJO-C, which show negative and positive responses before and after phases 5–6, respectively. The second term (Vχζ¯, advection of climatological absolute vorticity by the anomalous irrotational wind) is several times weaker than the first term immediately before day 0, but becomes important during days 0–6. The contributions from the third and fourth term are negligible. The sum of these four terms (black curve in Fig. 8) follows the first term closely most of the time, which suggests that the dominant contribution comes from climatological vortex stretching by the anomalous irrotational wind. The result is consistent with some previous studies (Hsu 1996; Seo and Son 2012; Henderson et al. 2017). However, some other studies concluded that the advection of climatological absolute vorticity by the anomalous irrotational wind (the second term) may also be important for the RWS (Lukens et al. 2017; Seo and Lee 2017; Tseng et al. 2019). The reason for this discrepancy remains unclear. In our case, further diagnosis indicates that the distinction of the first term between MJO-B and MJO-C here mainly comes from the anomalous divergence related to the tropical heating of MJO rather than the climatological absolute vorticity (not shown), suggesting that difference in climatological jet mean flow is not the cause for the RWS distinction. In general, stronger MJO-C precipitation anomalies excite stronger Rossby waves that propagate eastward and poleward.

Fig. 8.
Fig. 8.

Time series of composite 200-hPa linear Rossby wave source (black lines) and its four budget terms from the equation averaged near the jet region (25°–45°N, 90°–160°E) for (a) MJO-B and (b) MJO-C. Lag 0 is the first day in phase 5. All variables are smoothed as 5-day moving averages.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

b. Water vapor transport

To quantify moisture transport associated with MJO-B and MJO-C in the Northern Hemisphere, the column-integrated water vapor and moisture flux anomalies [(1/g)1000hPa1hPaqVdp, where g is the gravitational acceleration, p is the pressure, q is the specific humidity, and V is the horizontal wind vector] during lag 5–9 of each phase are shown in Fig. 9. Moisture transport associated with MJO is relatively weak during lag 5–9 of phase 1 (Figs. 9a,i). As the MJO-C convection over the Indian Ocean develops during phases 2 and 3, the meridional circulation associated with the enhanced Rossby waves can transport large amounts of moisture (∼3 kg m−2 higher than climatology) to southeastern Asia and the jet exit region during lag 5–9. Chen et al. (2021) further showed that the anticyclonic circulation associated with suppressed convection over the MC and western Pacific regions at earlier period may also contribute to the anomalous meridional transport of moisture over East Asia. These processes might result in higher rainfall in East Asia and the jet exit region (Jeong et al. 2008; Jia et al. 2011; Chen et al. 2021), as shown in Figs. 4j–l. The zonal scale of positive moisture transport in the midlatitude is larger in MJO-C, which expands to the northeastern Pacific and reaches the northwestern side of North America during lag 5–9 after phases 4 and 5, while moisture in Alaska and the southwestern United States shows significantly negative anomalies (∼1 kg m−2 lower than climatology; Figs. 9l,m). For MJO-B, however, there are negative moisture anomalies near the northeastern Pacific during lag 5–9 after phases 3 and 4 (these values are similar to that in the Alaskan region in the MJO-C case; Figs. 9c,d), and this is associated with the long-lasting positive Z500 anomalies over the North Pacific, which to some extent blocks the eastward transport of moisture in the midlatitudes through the anomalous easterlies south of the anticyclone near 30°N. During lag 5–9 after phases 5 and 6, the MJO-B moisture is transported toward North America (Figs. 9e,f) since the positive Z500 anomalies over the North Pacific collapse, and the southwest–northeast shape of Z500 anomalies also favors the northeastern transport through southwesterly flow, but it lags MJO-C by about one phase. During phases 7 and 8 (Figs. 9g,h,o,p), the negative moisture anomalies dominate in the jet exit region and the sign of spatial pattern is almost opposite to that during phases 3 and 4.

Fig. 9.
Fig. 9.

Composites of anomalous column-integrated water vapor (color shading; kg m−2) and column-integrated moisture flux (vectors; kg m s−1) during lag 5–9 days after each phase of (left) MJO-B and (right) MJO-C. Results significant at the 95% confidence level for column-integrated water vapor and flux are dotted and presented by thick black vectors, respectively.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

To the extent that the moisture fluxes can be used to represent the AR events, the above results indicate that the MJO barrier effect can modulate the AR life cycles. Zhou et al. (2021) found a link between the MJO and AR life cycles. During the MJO phases 2 and 4 when the enhanced (suppressed) convection is located over the Indian Ocean (western Pacific), AR events are more likely to originate over eastern Asia with less activity over the subtropical northern Pacific. The opposite occurs during phases 7 and 8. The moisture fluxes associated with MJO-B and MJO-C (Fig. 9) are broadly similar to Zhou et al. (2021), but the different amplitudes, pathways, and timing of extratropical moisture transport from MJO-B and MJO-C may play different roles in modulating the AR and extreme precipitation events at mid- to high latitudes (Stan et al. 2017).

c. PNA and NAO responses

The Z500 anomalies associated with MJO-C (Figs. 5 and 6) show a PNA-like pattern similar to that documented in some previous studies (Wallace and Gutzler 1981; Ferranti et al. 1990; Mori and Watanabe 2008; Roundy et al. 2010; Seo and Lee 2017), indicating that a negative (positive) PNA is more likely to occur after MJO phase 3 (7). The PNA wave train pattern typically consists of three or four alternating positive and negative centers at the Aleutian Islands, the Gulf of Alaska, Alberta, and the southeastern United States (Wallace and Gutzler 1981; Barnston and Livezey 1987). To quantify PNA, we compute the PNA index based on the rotational EOF analysis technique (REOF) for the monthly mean 500-hPa geopotential height anomalies in the Northern Hemisphere from all 12 calendar months during 1983–2019, following the method by Barnston and Livezey (1987). The PNA pattern is identified by inspecting the rotated modes, as shown in the appendix (Fig. A1). The PNA index is then calculated by projecting the daily 500-hPa geopotential height anomalies onto the spatial pattern and normalizing the corresponding time series.

Figure 10 shows the PNA index at various phases and time lags for MJO-B and MJO-C. The lagged composite of the PNA from both MJO-B and MJO-C displays slanted patterns in the time lag–MJO phase diagram (Fig. 10). The leftward slant leaves the impression of propagation relative to an observer moving eastward with the MJO, even though the PNA is overall stationary in longitude. The MJO-B PNA is negative and insignificant (∼−0.3) around lag 10 in phase 3 and 4, while it is slightly weaker during MJO-C during the same period. From phases 6 to 8, the PNA associated with MJO-C is positive and statistically significant (∼0.45) after around lag 10, and the positive signal lasts up to lag 20 after phase 8, while the response to MJO-B is marginally detectable. These results are consistent with previous studies (e.g., Seo and Lee 2017). Our results further reveal that the impact of MJO-C on PNA is stronger and more significant, especially from phases 7 to 8 when the MJO convection reaches western Pacific, while the impact of MJO-B is negligible since most PNA signals from phases 3 to 4 and phases 7 to 8 are insignificant. The PNA signal for MJO-C is asymmetric as the response in phases 7–8 is larger than in phases 3–4. This occurs likely due to the different spatial patterns of the extratropical response between phases 3–4 and phases 7–8 (Figs. 5 and 6). The wave train near the PNA region around phase 7 extends more eastward than that around phase 3, which is in better agreement with the canonical PNA pattern (Fig. A1), and the projection leads to higher PNA values.

Fig. 10.
Fig. 10.

Lagged diagrams of the PNA index (shading) in the eight MJO phases for (a) MJO-B and (b) MJO-C. Dotted regions indicate that the values are significant at the 95% confidence level.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

The NAO index is similarly calculated by projecting the daily 500-hPa geopotential height anomalies onto the NAO pattern from the corresponding rotated mode (Fig. A1), which is characterized by a north–south dipole located near Greenland and the North Atlantic Ocean (Wallace and Gutzler 1981; Barnston and Livezey 1987). Figure 11 shows the lagged composite of the NAO index. For MJO-B, the NAO is negative before phase 4 and turns positive in the subsequent phases, but the signal is insignificant during lags after all eight phases. In contrast, the NAO during MJO-C is positive (∼0.35) around lag 5 during phases 6 and 7, and negative (∼−0.35) around lag 15 during phase 8. Previous studies (Cassou 2008; Lin et al. 2009) found that positive (negative) NAO often occurs about 10 days after the MJO convection is enhanced over the tropical Indian Ocean (western Pacific Ocean) region. Our result is broadly consistent with this finding except that the phase of PII leads other commonly used MJO indices (e.g., RMM and OMI) by a few days. The NAO signal during MJO-C is stronger in phases 6–8 than in phases 2–4, and the significant positive and negative values occur at different lag days. The mechanism responsible for this asymmetric response is beyond our scope in this study.

Fig. 11.
Fig. 11.

As in Fig. 10, but for the NAO index.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

The above results show the different impacts of MJO-B and MJO-C on the two teleconnection patterns: PNA and NAO. The impact of MJO-C is stronger and more significant especially after phase 6 when the MJO active convection reaches the tropical Pacific, and this impact lasts for about 10 days after each phase. For MJO-B, its weak tropical precipitation anomalies and associated extratropical Rossby waves might be responsible for weaker PNA and NAO. Therefore, the MC barrier effect plays an important role for the tropical–extratropical teleconnections by modulating the intensity of the MJO convection that reaches the western and central Pacific Ocean.

d. High-latitude surface air temperature

There is compelling evidence that the high-latitude surface air temperature (hereafter SAT) can be influenced by the tropical MJO activity associated with poleward-propagating Rossby wave trains (Vecchi and Bond 2004; Lee et al. 2011; Yoo et al. 2011, 2012; Hu et al. 2019). To examine the different impacts of MJO-B and MJO-C on SAT at high latitudes, we compute the area-weighted averages of SAT anomalies over the high-latitude region near Alaska (60°–90°N, 180°–120°W), where the MJO influence on surface temperature achieves the maximum. We will refer to it as the Alaska-Arctic SAT.

Figure 12 shows the lagged diagram of Alaska-Arctic SAT in different phases of MJO-B and MJO-C. The results show significant differences in amplitude and timing between MJO-B and MJO-C. The Alaska-Arctic SAT during MJO-C shows significant negative anomalies after lag 10–15 during phases 2 and 3 and positive anomalies after lag 15 during phases 6 and 7. This is consistent with previous studies, which showed that the Arctic warming (cooling) often takes place about lag 10 during the MJO phase 5 (phase 1) (e.g., Yoo et al. 2012). The SAT responses during MJO-B are relatively weak and partly insignificant, while the response from MJO-C is greater than 1 K and the signals are mostly statistically significant. The lag patterns from MJO-C and MJO-B differ in some aspects: the SAT evolution from MJO-C shows a clear slanted pattern while the signal from MJO-B is more uneven, and the response to MJO-B lags behind MJO-C by ∼7 days, as also evident for Z500 in Figs. 5 and 6.

Fig. 12.
Fig. 12.

Lagged diagrams of anomalous SAT (shading; K) averaged over 60°–90°N, 180°E–120°W in the eight MJO phases for (a) MJO-B and (b) MJO-C. Dots indicate the signals are significant at the 95% confidence level.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

Change in the SAT near the Arctic associated with the MJO is dynamically driven, often referred as dynamical warming, including eddy-induced adiabatic warming and eddy heat fluxes as the dominant contributors (e.g., Lee et al. 2011; Yoo et al. 2012). For instance, Yoo et al. (2012) found that the increased (decreased) poleward eddy heat flux for zonal wavenumbers 1–3 associated with the MJO phase 5 (phase 1) can greatly contribute to the Arctic warming (cooling).

To further illustrate the processes related to the above differences in SAT between MJO-B and MJO-C, the spatial patterns of anomalous SAT in the polar region at different lag days in phase 3 of MJO-B and MJO-C are shown in Fig. 13. The Alaska-Arctic warming exists before phase 3 for both MJO-B and MJO-C (not shown) since there are anomalous easterly and poleward eddy heat fluxes over regions north of 60°N in this period. During lag days 5–10 after phase 3 of MJO-B (Figs. 13a,b), there is still relatively weak SAT warming (∼0.5 K) near Alaska associated with the southwest–northeast tilted positive Z500 anomalies over the North Pacific (Fig. 5) and corresponding poleward eddy heat flux. During lag 10–20 (Figs. 13c,d), SAT cooling appears as the westerly anomalies and equatorward eddy heat flux at high latitudes are strengthened, but the amplitude near Alaska is relatively weak (<1.5 K) and insignificant. For MJO-C, however, as the positive Z500 anomalies occur over the North Pacific around lag 5, the anomalous northwesterly wind develops and dominates near Alaska throughout lag days 5–20, which leads to equatorward eddy heat flux in the polar region and significant cooling (>3 K; Figs. 13e–h).

Fig. 13.
Fig. 13.

Composites of anomalous SAT (shading, K) at lag days 5, 10, 15, and 20 after phase 3 of (a)–(d) MJO-B and (e)–(h) MJO-C. Results significant at the 95% confidence level for SAT are dotted.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

The lagged composites in phase 7 are similarly shown in Fig. 14. The Alaska-Arctic SAT changes its pattern similarly as in phase 3 but with opposite signs for both MJO-B and MJO-C. The SAT cooling in MJO-B lasts during lag 5–10 at high latitudes (Figs. 14a,b) and is replaced by warming after lag 15 (both ∼2 K; Figs. 14c,d), while significant warming in MJO-C appears earlier and is stronger throughout this stage (>3.5 K; Figs. 14e–h), as its anomalous easterly and poleward eddy heat fluxes develop around lag 5, accompanied by the enhanced negative anomalies of Z500 over the North Pacific (Fig. 5).

Fig. 14.
Fig. 14.

As in Fig. 13, but for lagged composites after phase 7.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

5. Summary and discussion

The present study explores extratropical responses to varied tropical heating associated with the MJO conditioned upon the MC barrier effect. The precipitation-based index is used to identify the MJO events that successfully cross the MC (MJO-C) or are blocked by the MC (MJO-B) during extended boreal winter seasons (November–April) from 1983 to 2019. Lagged composites from the ERA5 reanalysis dataset reveal large differences in the extratropical responses between MJO-B and MJO-C on the intraseasonal time scale. Figure 15 presents a schematic summary for both the MJO-B and MJO-C teleconnections around phases 3 and 7. The main conclusions are summarized as follows:

  1. 1) The main difference between MJO-B and MJO-C in the Indian Ocean during phases 2 and 4 is their amplitude: anomalous precipitation and low-level wind during MJO-C are about 2 times stronger than MJO-B (Fig. 4). This is consistent with other studies using different tracking methods (e.g., Zhang and Ling 2017). This amplitude difference maintains and grows larger as the MJO enters and crosses the MC. The greatest difference occurs over the western Pacific during phases 7 and 8 after the passage of MC. On the other hand, their eastward propagation speeds are comparable.
  2. 2) The extratropical wave trains from central Asia to eastern North America show different structures during MJO-B and MJO-C (Figs. 5 and 6). The anomalous Z500 responses to MJO-C over the North Pacific and North America show significant wave trains from phases 3 to 4 and from phases 7 to 8, similar to the typical results based on strong MJO events. The wave train responses to MJO-B include long-lasting positive anomalies during phases 3 and 4 and weaker negative anomalies during phases 7 and 8, and southwest–northeast tilted wave structure over the North Pacific. The poleward-propagating Rossby waves are diagnosed using the 200-hPa linear Rossby wave source (RWS) function. The RWS associated with MJO-C is significantly stronger, especially around phases 3–4 and phases 7–8 (Fig. 7). Among the various RWS sources, stretching of climatological absolute vorticity by the anomalous divergence dominates throughout the period considered here (Fig. 8). In contrast, the RWS excited by MJO-B is insignificant due to the weak tropical heating.
  3. 3) The extratropical water vapor transport by the anomalous flow associated with MJO-B and MJO-C initiates in the MJO phase 2 (Fig. 9). The transport associated with MJO-C is stronger and brings more moisture to the southeastern Asia and jet exit region (near 30°N and 160°E), leading to enhanced precipitation anomalies in the subtropical East Asian regions. The anomalous moisture flux associated with MJO-C subsequently expands to the northeastern Pacific and northwestern North America during lag 5–9 after phases 4 to 5. The northeastward moisture transport during MJO-B is enhanced during lag 5–9 after phases 5 to 7 due to negative Z500 anomalies over the North Pacific and its southwest–northeast tilt.
  4. 4) The PNA teleconnection pattern shows positive values about 10–15 days after phase 6 from MJO-C (Fig. 10). The NAO index from MJO-C is positive about 5 days after phase 6 and 7, and negative about 15 days after phase 8 (Fig. 11). However, MJO-B shows insignificant impact on both teleconnections, likely due to the weakened tropical convection (especially over the Pacific Ocean after phase 6) and associated poleward-propagating Rossby wave trains.
  5. 5) The anomalous Alaska-Arctic SAT values (60°–90°N, 180°E–120°W) in MJO-B and MJO-C are also significantly different (Figs. 1214). The amplitude of the SAT after MJO-B is evidently weaker and largely insignificant and lags MJO-C by a few days. The differences in SAT response are likely due to the different meridional eddy heat flux associated with the extratropical circulations (e.g., the Z500 anomaly over the North Pacific). The lagged and weak positive SAT anomaly in phases 7 and 8 of MJO-B might be due to its weak extratropical circulation. The amplitude of SAT response is significantly stronger in MJO-C due to stronger poleward-propagating Rossby waves and correspondingly larger eddy heat flux.
Fig. 15.
Fig. 15.

Schematic diagrams of different extratropical responses associated with anomalous precipitation during MJO-B and MJO-C, showing results around (a),(b) phase 3 and (c),(d) phase 7. The enhanced (suppressed) convection of MJO is indicated by “Conv” (“Supp”). The positive (negative) column-integrated water vapor anomaly is indicated by “Wet” (“Dry”), due to the anomalous moisture flux from lower (higher) latitudes to higher (lower) latitudes. The RWS, anomalous wind, and MJO teleconnection are represented by light brown arrows, thick black arrows, and green dashed arrows, respectively. The red (blue) arrow denotes the warm (cold) advection. The extratropical teleconnections (e.g., PNA and NAO) may occur with a 7–10-day delay with respect to tropical convection.

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

As shown in Fig. 15, the main distinction in the extratropical response between MJO-B and MJO-C is likely to emerge around phases 3 and 7, during which the MJO-related tropical convection generally shows a dipole structure in the eastern Indian and western Pacific Oceans. The RWS from the dipolar tropical forcing grows stronger after these phases of MJO-C, which excites Rossby waves propagating toward the North Pacific and North American regions, and further leads to the anomalous circulation patterns modifying local weather (Seo and Lee 2017; Tseng et al. 2019). Aside from the important role of the MC barrier effect, the amplitude differences in the Indian Ocean (phases 2 and 4) may also contribute to the contrasting teleconnection patterns between MJO-B and MJO-C.

In summary, results from the present study suggest that understanding the cause of diverse extratropical responses modulated by the MC barrier effect during these MJO phases is important to future improvement in subseasonal prediction. As shown from our results, the relatively weak and insignificant extratropical impacts of MJO-B due to the MC barrier effect in phases 7 and 8 are attributed to the weak tropical convection signal over the western Pacific. However, the mechanism responsible for the differing tilted pattern and timing from MJO-B and MJO-C seems to be ambiguous. Both the amplitude and structure of tropical convection associated with the MJO might contribute to the different extratropical responses. Lee et al. (2019) found that ENSO can modulate the MJO-related Rossby wave trains and teleconnections. However, our composites of sea surface temperature anomalies during MJO-B/C events show weak and statistically insignificant signal (not shown), so the effect of ENSO is inconclusive given that there are only a limited number of ENSO events on record. In summary, our results suggest that the MC barrier effect can substantially influence the amplitude and distribution of tropical heating and result in significant variation of extratropical responses. We suggest that improved prediction of the MC barrier effect will benefit subseasonal prediction of both tropical and extratropical weather. The mechanisms of the MC barrier effect on the MJO and the remote impact are both important future research topics.

Acknowledgments.

The authors acknowledge the funding support of National Natural Science Foundation of China 41875066 and 41875067. We are grateful to three anonymous reviewers for their insightful comments, which led to significant improvement in the presentation and interpretation of the results.

Data availability statement.

The TRMM 3B42 data version7 (https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_7/summary) are available from the National Aeronautics and Space Administration (NASA). The PERSIANN-CDR data (https://chrsdata.eng.uci.edu) are available from the developers of Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). The ERA5 reanalysis data (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview) are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).

APPENDIX

PNA and NAO Patterns

We compute the PNA and NAO patterns following the method outlined in Barnston and Livezey (1987). We first remove the monthly mean climatology of the 500-hPa geopotential height derived from ERA5 reanalysis by subtracting the first three harmonics of the annual cycle for the 1983–2019 period. The rotational EOF (REOF) technique is then applied to the resultant monthly anomalies in the Northern Hemisphere from all 12 calendar months. The PNA and NAO are identified by inspecting the obtained rotated modes, as shown in Fig. A1.

Fig. A1.
Fig. A1.

The (a) PNA and (b) NAO patterns obtained from the REOF analysis for monthly mean 500-hPa geopotential height anomalies (m).

Citation: Journal of Climate 36, 1; 10.1175/JCLI-D-21-0492.1

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Supplementary Materials

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