Quasi-biweekly oscillation of the Asian monsoon rainfall in late summer and autumn: different types of structure and propagation
This study contrasts two types of quasi-biweekly oscillations (QBWOs) over tropical Asia in late-summer and autumn (from August to October). Using a tracking method to calculate the frequency of QBWO events over the Asian monsoon region, two types of QBWOs in monsoon rainfall are revealed. One originates from 110° to 140°E and propagates westward to southern China with a notable impact on the regional rainfall, while the other initiates from 160°E to the dateline and does not affect southern China rainfall significantly. Analysis of the vertical structure of moisture flux shows that the moisture source for type 1 events is dominated by the zonal flux component and that for type 2 the meridional flux component. The nature of the moisture flux determines whether the oscillation can propagate across 120°E and affect rainfall over southern China. Results also show that the strength of the South Asian high and the western Pacific subtropical high differently modulate the generation of the two types of QBWOs. Specifically, mutually stronger (weaker) highs favor the first (second) type of the oscillation. A close relationship also exists between the QBWOs and western Pacific sea surface temperature (SST) anomalies, suggesting that the SST anomalies can potentially trigger the QBWOs.
KeywordsQuasi-biweekly oscillation Intraseasonal variability Boreal summer intraseasonal oscillation Asian monsoon
Two dominant intraseasonal oscillation (ISO) modes exist in the Asian summer monsoon system: a 10–20-day oscillation, which is also called the quasi-biweekly oscillation (QBWO), and a 30–60-day oscillation. The QBWO was perhaps first discovered over the Indian Ocean region (Keshavam 1971; Keshavamurty 1972). It propagates westward and is closely linked to activity (e.g., active and inactive periods) of the monsoon. Previous studies have compared the QBWO with the 30–60-day oscillation and found many differences between the two modes (Chen and Chen 1993, 1995; Chen et al. 2000; Annamalai and Slingo 2001; Yokoi et al. 2007; Yang et al. 2008; Li et al. 2015). Wang et al. (2009) revealed that the quasi-biweekly and 30–60-day modes of intraseasonal variation are characterized by different source regions and life cycles. However, compared to the Madden–Julian Oscillation (MJO) and other 30–60-day oscillations (Zhang et al. 2013; Ling et al. 2017a, b), the characteristics of QBWOs are much less understood, especially when considering the maintenance mechanisms and the effects of the QBWO on weather and climate outside the tropics.
QBWO activity is clearly seasonal and regional, much like for the 30–60 day boreal summer ISO where the climatological propagation characteristics of convective anomalies in the Indian Ocean to western Pacific are quite different from May to June (MJ) that during ASO (Kemball-Cook and Wang 2001). In the Asian monsoon region, the summer QBWO propagates westward, and the main genesis region for disturbances is the western Pacific Ocean (Kikuchi and Wang 2009; Jia and Yang 2013). Fukutomi and Yasunari (1999) noted that on the time scale of 10–25 days, convection was associated with well-organized low-level cyclonic circulation anomalies over the South China Sea. Annamalai and Slingo (2001) showed that QBWO is more regional than 30–60 day variability, and the QBWO is mainly generated in the western Pacific with prominent zonal propagation. The propagation characteristics of the QBWO vary substantially with month of the year given its diverse origins in different seasons (Wen and Zhang 2008; Chen and Sui 2010; Wen et al. 2010). Although extra-tropical processes over land can affect the intraseasonal variability of monsoon rainband (Wang et al. 2017b), Wang et al. (2017a) showed that the boreal summer QBWO in the Asian monsoon region has its primary origins in the western equatorial Pacific and it propagates northwestward into the Bay of Bengal. Therefore, we focus on the QBWO in the Asian monsoon region in this study. While the study of Kikuchi and Wang (2009) emphasized global behavior of QBWOs, here we focus on regional QBWO events that begin in the western Pacific and propagate westward.
The paper is organized as follows. In Sect. 2, we describe the data sets and methodology. We modify the MJO tracking method of Zhang and Ling (2017) for use in tracking QBWO events. In Sect. 3, the propagation characteristics and vertical structures of two types of QBWOs are discussed. In Sect. 4, the large-scale circulation anomalies associated with QBWOs are investigated. Summary and discussion are given in Sect. 5.
2 Data and methodology
To represent QBWO events in Asian monsoon rainfall, we use daily rainfall data from 1996 to 2015 from the Global Precipitation Climatology Project, version 1.2 (Huffman et al. 2001, 2016), with a horizontal resolution of 1° × 1°. ERA-Interim data from the European Centre for Medium-range Weather Forecasts (Simmons et al. 2007) with a resolution of 0.75° × 0.75° are used to study dynamic and thermodynamic atmospheric fields during QBWO events. We also use daily sea surface temperature (SST) derived from the NOAA high-resolution (0.25° × 0.25°) blended analysis, OISSTV2 (National Climatic Data Center 2007; Reynolds et al. 2007; Banzon and Reynolds 2013), which includes daily SST and sea ice. The Real-time Multivariate MJO (RMM) index of Wheeler and Hendon (2004) is used to analyze the relationship between the QBWO and MJO. The RMM index is derived using a multivariate EOF analysis on equatorial wind anomalies in the upper and lower troposphere and outgoing longwave radiation anomalies.
2.2 Tracking method
As discussed earlier, EOF analysis is more suitable for MJO study (e.g. Lin 2012; Lee et al. 2013; Maloney et al. 2014) than for QBWO study, since the first two EOF modes derived from unfiltered anomalies are dominated by 30–60 day variability. Considering that QBWOs have higher variance but are less spatially-coherent, tracking methods seems to be a more effective means of analyzing the QBWO. Following the method of Zhang and Ling (2017), which objectively identified the eastward movement of positive rainfall anomalies along the equator, we track the QBWO using GPCP rainfall anomalies. This method focuses primarily on 1D motion in longitude. The method of Zhang and Ling is able to provide several characteristics of the identified events (Fig. 2 in Zhang and Ling 2017), such as the starting longitude and time, ending longitude and time, propagation speed and range, strength, life span, and zonal scale. These quantities are not provided by conventional EOF-based indices. The Zhang and Ling (2017) method could also be conceivably used in other tropical or extra-tropical wave disturbances. For example, easterly waves or convectively coupled Rossby waves could be tracked by changing the settings of the tracking method or by preprocessing the data to preselect certain wavenumbers and frequencies.
In the data preparation stage, the original tracking method uses the fast Fourier transform (Gottschalck et al. 2013) to obtain eastward-propagating intraseasonal (20–100-day) precipitation signals using equatorial symmetric data. Since the strongest QBWO signals in summer are over the Asian monsoon region (not equatorially symmetric), we replace the fast Fourier transform with the Lanczos filtering (Duchon 1979) to get 10–20 day subseasonal signals.
After filtering, rainfall anomalies are averaged between 10°–25°N.
For better identifying the tracking lines which show QBWO events, nine-point local smoothing is performed as the last step of data preparation.
In the tracking stage, the analysis domain is from 40°E to 160°W.
Only data from late summer to early autumn (from August to October) are used for 1997–2015.
The interval between two tracked events is required to be longer than 5 days (for MJO, its interval is from 20 to 25 days), with characteristics of propagation speed and range discussed in more detail below.
Considering that the QBWO primarily originates over the western Pacific and propagates westward (Kikuchi and Wang 2009), we set a reference longitude to 120°E for effectively identifying MJO events over the western Pacific (about 90°E in Zhang and Ling 2017) and only track westward movement. Because of this, all tracking lines will be positive.
2.3 Other analysis methods
Lanczos filtering (Duchon 1979) is applied to extract QBWO signals from OLR and atmospheric circulation fields based on a 10–20-day filtering window. For the 10–20-day oscillation, a Lanczos digital filter with 61 daily weight coefficients would provide a sharp cutoff response with negligible Gibbs oscillation. In addition, a composite analysis approach is applied to investigate the large-scale features associated with the QBWOs. The Student’s t test is used to assess the statistical significance of the results obtained.
3 Propagation and vertical structures of QBWO
3.1 Definitions of QBWO types and their propagation characteristics
Using the tracking method defined in Sect. 2, we identified a total of 88 events from the 1997–2015 rainfall data. We first obtained statistics about propagation speed, propagation range, and zonal scale, and then removed irrelevant events on this basis (see “Conclusions and discussion” below).
Based on the aforementioned QBWO attributes, we chose QBWO events with westward propagation speed between 2 m s −1 and 10 m s −1, which results in 21 events (23.9%) being excluded. We also choose events with propagation distance greater than 20°, which resulted in the exclusion of an additional 11 events (12.5%). A total of 59 QBWO events are thus retained in our analysis.
3.2 Three dimensional QBWO structure
3.3 Life cycles of QBWO events
Previous results using EOF-based methods have indicated that QBWOs originate mainly near 160°E and strong QBWOs can propagate to 110°E. An important finding from our study is that the QBWO events generated near 160°E are not necessarily linked to those events that initiate and propagate near 110°E. There are two kinds of QBWO events with different origins, propagation paths, and ranges of influence.
The positive anomaly of the type-1 QBWO propagates to East Asia where it affects precipitation before decaying. However, the positive anomaly of the type-2 QBWO decays over the topography of the Philippine Islands, and has little effect on mainland Asia. Comparing these two types of QBWO events, topography and interaction with land may be important factors in determining the propagation of QBWOs.
4 Variation of large-scale features associated with QBWO propagation
4.1 Relationships with western Pacific subtropical high and South Asia high
The western Pacific subtropical high (WPSH) and the South Asia high (SAH) are two important systems related to the Asian summer monsoon. Previous have studies suggested that variability of the WPSH and SAH can affect the Asian summer monsoon, and it is logical to expect an influence on QBWOs since they are also an integral part of precipitation variability in this region. Yang et al. (2014) found that the SAH and the WPSH, which influence the evolution of QBWOs, move toward (away from) each other in early (late) summer. Wang et al. (2016) suggested that the intensity and extent of the WPSH might affect the evolution and propagation of QBWOs by modulating the occurrence and characteristics of convection. To further explore the interactions between the different types of QBWOs and the large-scale circulation, we now focus on WPSH and the SAH variations on both 10–20-day and climatological time scales in the context of their interactions with QBWOs.
The results above suggest that the interactions between subtropical highs and QBWOs may be different at different time scales. On the 10–20-day time scale, the geopotential height anomalies associated with QBWOs perturb the WPSH and SAH circulations. The 10–20-day filtered height anomalies associated with the SAH at 200 hPa are characterized by a northward propagation. Longer term variations in the SAH and WNSH can also affect the type of QBWO that is produced. Simultaneously stronger SAH and WPSH are more conducive to type-1 QBWO events, while simultaneously weaker SAH and WPSH are more beneficial for type-2 QBWO events.
4.2 Relationships with Pacific SST and the Madden–Julian Oscillation
In addition to the local SST anomalies that can directly influence QBWO convection, it is worth noting that in the eastern equatorial Pacific there exist also significant positive (Fig. 13a) and negative (Fig. 13b) SST anomalies, likely reflecting El Niño-Southern Oscillation (ENSO) events. The change in monsoon ISOs with ENSO evolution has been an important research topic in recent years. In El Niño developing summers, the 30–60-day oscillation has been suggested to be stronger than the QBWO (Wu and Cao 2017), although these results were not conclusive. Yang et al. (2008) indicated that year-to-year variations in the intensity of the QBWO and of the 30–50-day oscillation over the South China Sea were anti-correlated during June–July. Pillai and Chowdary (2015) found that the ISO characteristics such as variance, northward propagation, spatial distribution, and durations of active and break days were strongly modulated by the seasonal background anomalies over the Indo-Pacific region. El Niño phases exert a stronger impact on the 30–60-day time scale than on the 10–20-day time scale. Liu et al. (2016) found that during El Niño summers, the ISO in the western North Pacific was dominated by a higher-frequency oscillation with a period around 20–40 days, whereas during La Niña summers the ISO was dominated by lower-frequencies between 40 and 70 days. Wu and Cao (2017) argued that the feedback of surface heat flux onto the intensity of atmospheric oscillations is more prominent on the 10–20-day time scale than on the 30–60-day time scale. The 10–20-day oscillation is stronger during El Niño-developing summers, whereas the 30–60-day oscillation is stronger during La Niña decaying summers. In general, the above results suggest a role for air-sea interaction and background SST state in the occurrence of QBWO events, although the precise nature of the modulation is sometimes contradictory.
We further examine whether QBWO events are modulated by the MJO. For this, the phases of the RMM index on the days of QBWO event initiation are shown in Fig. 14. In La Niña developing summers (Fig. 14a), the type-1 QBWO events tend to occur when the MJO is in any phase. However, in El Niño developing summers (Fig. 14b), the emergence of a QBWO event appears most likely in phases 1, 6, 7 and 8 of the MJO. This suggests that when a positive MJO convective event is present over the Indian Ocean or the Maritime Continent (in phase 2, 3, 4 and 5), it is not conducive to the occurrence of QBWOs. In general, the impact of MJO on the QBWO does not appear to be substantial based on this preliminary analysis and requires further study.
5 Conclusions and discussion
The QBWO and the 30–60-day oscillation are two primary modes of intrseasonal variability in the tropical and subtropical summer monsoons. While the 30–60-day oscillation has been investigated extensively, many features of the QBWO remain unclear, especially its complex generation and propagation mechanisms. The present study is focused on the QBWO of Asian monsoon rainfall during August–October. Building on the MJO tracking algorithm of Zhang and Ling (2017), we depict the westward propagation of QBWOs over the monsoon region using an improved tracking method.
Previous studies (Yang et al. 2014; Wang et al. 2016) have compared different paths of QBWO propagation at different stages of monsoon development. Our study shows that different types of QBWO propagation can occur in the same season. The tracked QBWO events can be divided into two categories. One originates from 110° to 140°E and exerts its influence westward to southern China. The other is characterized by initiation locations from 160°E to the dateline, and shows a weaker effect on mainland China. Our results suggest that environmental conditions over the South China Sea after disturbances pass through the northern mountains of the Philippines are important for determining whether these QBWO events can affect the Asian continent.
Analysis of the vertical structure of moisture flux convergence shows that the moisture source for type-1 QBWO disturbances is mainly the zonal flux component, and for type 2 disturbances the meridional flux component. The difference in moisture sources for the two types of QBWOs provides clues into their different generation mechanisms.
The SAH and the WPSH play an important role in the generation and propagation of QBWOs. On the 10–20-day timescale, both SAH and WPSH are modulated by wave trains associated with the QBWOs. WPSH geopotential height anomalies appear most strongly modulated by westward-propagating QBWO wave trains. However, the SAH is primarily modulated by QBWO wave trains that propagate northward. Associated with longer timescale fluctuations of the SAH and WPSH, type-1 QBWO events are more likely to occur when both SAH and WPSH are stronger, while type-2 QBWO events correspond to simultaneously weaker SAH and WPSH. Previous studies (e.g., Tao and Xu 1962; Tao and Wei 2006) have suggested that the SAH and the WPSH were closely related to each other. This coupling of the SAH and WNPSH strongly modulates the meridional circulation, vertical motion, and summer monsoon rainfall that affect QBWOs, although the responsible physical mechanisms are not entirely clear. The multi-scale dynamic processes linking SAH and WPSH to the QBWO events originating in the western Pacific are an issue that deserves further investigation.
More generally, the physical mechanisms underlying QBWO dynamics and it variability are still not fully understood. It is believed that the QBWO is an intrinsic mode of the tropical atmosphere (Goswami and Mathew 1994; Chatterjee and Goswami 2006), and may be supported by an evaporation-wind feedback under conditions of mean westerly flow or convective heating induced by boundary layer convergence. However, it has also been suggested that feedbacks among convection, Rossby wave dynamics, and the low-level circulation are crucial in maintaining observed QBWO structure (Wen and Zhang 2008). SST anomalies are important for determining convection activity and are closely related to the origin and development of intraseasonal oscillations. Our study indicates that SST anomalies in the South China Sea to the west of the Philippines may be an important factor for the occurrence of different types of QBWO events. However, the air-sea interaction processes by which the SST anomalies affect QBWO propagation needs further investigation.
A recent study by Liu et al. (2016) on differences in ISO activity over the South China Sea and the western Pacific during different stages of ENSO showed that during El Niño developing summers the ISO is dominated by a higher-frequency oscillation with a period around 20–40 days, whereas during La Niña developing summers the ISO is dominated by a lower-frequency period around 40–70 days. On shorter timescales, Wu and Cao (2017) found that the 10–20-day oscillation was enhanced during El Niño developing summers, whereas the 30–60-day oscillation was enhanced during La Niña decaying summers. Our results complement the work above and show that during August–October the type-1 QBWO events are more likely to occur in La Niña developing summers, and the type-2 QBWO events more likely in El Niño developing summers, especially during phases 1 and 6–8 of the MJO.
Based on the above results, we can confidently say that the occurrence of type-1 QBWO events affects rainfall over the Asian continent. Thus, the simultaneous enhancement of SAH and WPSH can be considered as a precursor for QBWO events that affect the Asian continent, which would be useful information for short-term forecasts. The SST in the South China Sea is also an important factor for whether QBWOs reach Asian, and how such SST variability affects QBWO characteristics deserves further investigations.
We appreciate several discussions with Prof. Mingfang Ting at the Columbia University and Dr. Mingting Li at the Sun Yat-sen University. The comments from two anonymous reviewers improved the overall quality of the paper. The study was supported by the National Key Research and Development Program of China (2016YFA0602703), the National Natural Science Foundation of China (Grants 41030020, 41705030, 41661144019, 41690123, and 41690120), the “111-Plan” Project of China (Grant B17049), and the Jiangsu Collaborative Innovation Center for Climate Change. EDM was also supported by Office of Naval Research Grant N00014-16-1-3087.
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