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--> --> -->Interannual variability in the seasonal transition that constitutes the broadscale monsoon onset has been shown to be related to thermal conditions over the Tibetan Plateau (Wu et al., 2012), El Ni?o-Southern Oscillation (ENSO) effects (Zhou and Chan, 2007; Hu et al., 2014; Xie, 2016; Zhu and Li, 2017), regional air-sea interactions (He and Wu, 2013) and intraseasonal oscillations (ISOs; Wu et al., 2010; Li et al., 2013; Zhu and He, 2013; Shao et al., 2015; Wang et al., 2018). (He et al., 2017) carried out a comprehensive analysis of the SCSSM onset in individual years between 1997 and 2014 and showed that the years can be divided into "normal", "intermittent" and "delayed" onset years based on the development of local circulations, thermodynamic conditions and rainfall patterns following the seasonal transition. (He et al., 2017) found that eight out of the 18 years they analyzed exhibited intermittent rainfall onset (such that the seasonal dynamical transition is not closely followed by the establishment of monsoon rains and maximum SCS surface temperatures, with a delay caused by an active ISO or northern cold air entering the SCS), and suggested that this reduces the potential predictability of local rainfall onset even if the seasonal dynamical transition may be predictable. (Wang et al., 2018) described the effects of the tropical ISO on early, normal and late SCSSM onsets observed over 34 years. They confirmed work from previous studies showing that, before each onset, the SCS is controlled by the dry phase of the ISO (Shao et al., 2015), and the SCS is warmed to precondition the onset; while after each onset, the SCS is cooled by the wet phase of the ISO (Wu et al., 2010). However, (Wang et al., 2018) showed that the transition process is found to be related to different ISO evolutions over the Indian Ocean for the three types of onsets.
Even in non-intermittent onset years, the progression of rainfall onset over East Asia is rarely smooth. After an initial burst of rainfall over the SCS, the rain band rapidly advances northward before stagnating over the Yangtze and Huai River valleys in the mei-yu front (baiu in Japan). The mei-yu rainband exhibits large intraseasonal and interannual variability and has been the subject of extensive literature [see (Ding and Chan, 2005) for a review]. Its onset is associated with a northward shift of the Northwest Pacific subtropical high axis to about 25°N and the migration of the upper-level westerly jet over Eurasia to the north of the Tibetan Plateau (Sampe and Xie, 2010; Luo et al., 2013). (Li et al., 2018) showed that the anticyclone in the upper troposphere over South Asia in April has a significant relationship with mei-yu onset dates, such that a stronger South Asian anticyclone in April is followed by earlier onset dates of the mei-yu. Despite the complexity associated with these multiple drivers, interannual variability in the seasonal transition that constitutes the broadscale monsoon onset for the Asian summer monsoon as a whole still provides useful information for forecasters about possible delays or early occurrence of the onset of rainfall over East Asia. One of the most-used indices for determining SCSSM onset is that proposed by (Wang et al., 2004). This index identifies the first pentad after 25 April in which the zonal wind at 850 hPa over the southern part of the SCS (5°-15°N, 110°-120°E) shifts from a mean easterly to a mean westerly. (Wang et al., 2004) demonstrated that this index is highly indicative of the seasonal transition of the large-scale circulation. They showed that the onset variations determined using this index matched the broadscale onset determined by the principal component of the first empirical orthogonal function (EOF) of the low-level winds over East Asia and the western North Pacific. They argued that this simple index avoids the additional complications of the intraseasonal variability that is included in EOF analysis.
An alternative definition for SCSSM onset was proposed by (Gao et al., 2001), and is used in operational extended-range forecasting by the China Meteorological Administration (CMA; D. Zhang, personal communication). This includes an additional criterion of a sustained increase of equivalent potential temperature at 850 hPa above 340 K over the SCS region (10°-20°N, 110°-120°E) concurrent with the establishment of westerly winds over the same region. The increase in equivalent potential temperature is considered to indicate sea surface warming, monsoonal transport of moisture into the region and the potential for increased convective activity (Gao et al., 2001; Li et al., 2013; Luo et al., 2013). The region specified by (Gao et al., 2001) is further north than that for the (Wang et al., 2004) index and includes the northern SCS.
In this paper, we investigate the prediction skill of the SCSSM onset on seasonal time scales in the operational hindcast set of the GloSea5-GC2 seasonal forecasting system. Section 2 outlines the data and methods used in our study; section 3 shows the analysis of predictability of the two onset indicators, including tests of the robustness of the seasonal forecast skill. Discussion and conclusions on the usefulness of the seasonal forecast skill of the broadscale monsoon onset using these SCSSM onset indicators are included in section 4.
Data representative of observations are taken from ERA-Interim (Dee et al., 2011) for the same years. Equivalent potential temperature is calculated from the temperature and humidity fields at 850 hPa using the formula in (Bolton, 1980). Sea surface temperatures (SSTs) for March (used in section 3.3) are taken from the HadISST1.1 dataset (Rayner et al., 2003).
SCSSM onset is determined using the criteria established by (Wang et al., 2004) and (Gao et al., 2001). According to (Wang et al., 2004), the onset date is the first pentad after 25 April (i.e., pentad 24 onwards) when the area-averaged U850 over the southern SCS (5°-15°N, 110°-120°E, denoted U SCS) is (i) greater than 0 m s-1; (ii) in the subsequent four pentads (including the onset pentad), U SCS must be positive in at least three pentads; and (iii) the accumulated four-pentad mean U SCS is greater than 1 m s-1. (Wang et al., 2004), (He and Zhu, 2015) and (Zhu and Li, 2017) compared the SCSSM onset pentads between different reanalyses, including NCEP-1 and -2 [(Kalnay et al., 1996) and (Kanamitsu et al., 2002) respectively], as well as ERA-Interim, and showed reasonable correlations between them (generally >0.8).
(Gao et al., 2001) suggested an onset criterion based on the area-averaged 850-hPa pentad equivalent potential temperature (θ e) and U850 over the region (10°-20°N, 110°-120°E), with the onset date being the first pentad when θe is greater than 340 K and U850 is greater than 0.0 m s-1, stably (i.e., persists for at least three pentads followed by a break of no more than two pentads, or for two pentads followed by a break of no more than one pentad). It should be noted that the region of consideration for this index is slightly further north than that considered by (Wang et al., 2004).
3.1. Prediction skill of SCSSM onset using the (Wang et al., 2004) criterion
Figure 1 shows the SCSSM onset pentads identified using the (Wang et al., 2004) criterion for each forecast member with start dates of 17 and 25 March, and 1 and 9 April, in each year, with the ensemble-mean pentad and that identified in the reanalyses. The average interannual standard deviation of onset dates from individual ensemble members is 2.2 pentads, which compares reasonably well with that of the reanalyses (2.6 pentads), and there is a statistically significant (at the 0.75% level, for a one-tailed t-test) correlation of 0.5 between the interannual variations of the ensemble-mean dates and those from the reanalyses, indicating significant predictability. The hindcasts also predict the mean onset pentad to match that of the reanalyses, i.e., pentad 28 (16-20 May).
Figure1. Predictability of the SCSSM wind onset: onset pentads derived using the method proposed by (Wang et al., 2004) from the GloSea5 ensemble predictions initialized on 17 and 25 March, and 1 and 9 April (green dots represent individual members of the 52-member ensemble, with the size of the dot scaled by the number of members predicting the same onset pentad), and their ensemble mean (green line), compared with the equivalent onset pentads derived from ERA-Interim (black line). The yellow line shows the Ni?o3.4 SST anomaly in March for each year taken from the HadISST1.1 dataset. Pearson correlation coefficients are given in the legend: r (ens, obs) represents the correlation between the GloSea5 ensemble mean and ERA-Interim; r (ens, sst) represents the correlation between the GloSea5 ensemble mean SCS onset pentads and the observed March Ni?o3.4 SST anomaly; r (obs, sst) represents the correlation between the ERA-Interim SCS onset pentads and the observed March Ni?o3.4 SST anomaly.(Luo and Lin, 2017) suggested that a more objective measure of the SCSSM onset can be determined using a daily cumulative U SCS and specifying the onset as where this time series changes from decreasing to increasing (indicating that the flow is becoming predominantly westerly). (Wang et al., 2004) also checked their SCSSM onset dates against a cumulative U SCS criterion, DU, which compares the accumulated U SCS in the three days prior to and after the onset. They showed that, although their onset criteria do not explicitly require an abrupt change in westerly speed across the onset pentad, the resultant onset pentads were coincident with such a change. We found that including the additional criterion of DU >7 m s-1 makes very little difference to our results (not shown).
We carried out the same analysis for four start dates (1, 9, 17 and 25) in January, February and March taken from the standard operational hindcast ensemble of seven members per start date, and also for a 56-member combined ensemble using start dates of 25 March, and 1, 9 and 17 April (see Table 1). The correlation coefficient increases with decreasing lead time, becoming statistically significant at the 1.5% level (for a one-tailed t-test) from February start dates onwards. Thus, there is significant skill in the SCSSM onset prediction using the (Wang et al., 2004) index at a near three-month lead time over this hindcast period.
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3.2. Predictability of SCSSM onset using the (Gao et al., 2001) criterion
Figure 2 shows the SCSSM dates identified using the (Gao et al., 2001) criterion in each year by each of the 52 ensemble members with start dates of 17 and 25 March, and 1 and 9 April, with the ensemble-mean pentad and that identified in the reanalyses. In contrast with the findings using the (Wang et al., 2004) U SCS index, we find low skill in onset prediction using the (Gao et al., 2001) index at a greater than one-month lead time. Table 1 shows that the correlation increases slightly if the lead time is reduced to around one month, but remains barely statistically significant at the 6% level (using a one-tailed t-test).
Figure2. As in Fig. 1 but for SCSSM thermodynamic onset as determined by a sustained increased of θ e above 340 K accompanied by the establishment of westerly winds over the region (10°-20°N, 110°-120°E), as proposed by (Gao et al., 2001) [with the threshold modified by (Ding and He, 2006)].The difference in prediction skill between the two methods of determining SCSSM onset may be in part related to the region used for the (Gao et al., 2001) index; (Wang et al., 2004) commented that: "… the northern SCS is open to the invasion of a cold front from the north. The westerly flow occurring before the onset is located north of the subtropical ridge and is not of tropical origin". They stated, therefore, that the northern part of the SCS should be excluded when defining the tropical monsoon burst over the SCS. (He et al., 2017) also commented on the influence of northern cold air entering this region of the SCS contributing to ambiguous or intermittent onset. They highlighted the case of 2009, when the strong westerly flow established in mid April was interrupted by easterlies propagating from the northern SCS for several days in early May. Other examples of years where this occurred were given in He et al. (2017, Figs. 1 and 2), and included 2007, 2009 and 2011. Additionally, although (He et al., 2017) did not identify 2004 as an intermittent onset year, the U850 averaged over the (Gao et al., 2001) SCS box fluctuates between easterly and westerly during May, making the onset ambiguous when the (Gao et al., 2001) index is used. He et al. (2017, Fig. 1) showed that this is related to the variability of the winds in the northern part of the SCS. In contrast, the U850 winds over the southern part of the SCS [as covered by the (Wang et al., 2004) box] do not fluctuate to the same extent. (Chan et al., 2000) showed that, in 1998, incursion of cold air into the northern SCS promoted the release of convective available potential energy, which helped to trigger the onset earlier than may have been expected given the ENSO conditions. (Liu et al., 2002) further linked the cold-air incursion to a Rossby wave train triggered over the Bay of Bengal.
The additional influence of variability from the subtropics in the northern SCS, which, like ISO, is unpredictable on seasonal time scales, is likely to be a contributing factor in the reduced seasonal prediction skill for SCSSM onset using the (Gao et al., 2001) criteria. In recognition of this, forecasters at CMA release their SCSSM onset forecasts using the (Gao et al., 2001) criteria only on the extended range (11-30 days) timescale (D. Zhang, personal communication, 30 March 2018), on which models have been shown in previous work to possess skill in predicting intraseasonal variability (e.g., Lee et al., 2015; Lim et al., 2018).
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3.3. Drivers of SCSSM onset predictability using the (Wang et al., 2004) index
Several studies have shown that ENSO is one of the main drivers of large-scale interannual variability in the Asian monsoon region (e.g., Zhou and Chan, 2007; Luo et al., 2016). Westerly (easterly) equatorial wind anomalies associated with El Ni?o (La Ni?a) and a weaker (stronger) Walker circulation are typically associated with negative (positive) SST anomalies over the SCS and a delayed (advanced) seasonal transition (He et al., 2017). This relationship is not symmetrical, however: (He et al., 2017) suggested that both ISOs and changes in west-east thermal contrasts across the Indian Ocean and western Pacific can influence the timing of onset in La Ni?a years. (Hardiman et al., 2018) found a similar asymmetry in the relationship between seasonal mean Yangtze River rainfall and ENSO in observations and hindcasts.We also show on Fig. 1 the observed March Ni?o3.4 SST anomaly time series from HadISST1.1 (yellow line). The correlation coefficient between the ensemble-mean SCSSM onset pentad time series derived using the (Wang et al., 2004) index and the Ni?o3.4 SST time series is 0.9, indicating that the predictable component of the hindcast SCSSM onset is driven mainly by ENSO, which itself is highly predictable on this time scale in GloSea5 (Scaife et al., 2014; MacLachlan et al., 2015). The correlation between observed estimates of SCSSM onset and the observed March Ni?o3.4 SST is rather lower (0.41), indicating the influence of other drivers of SCSSM onset variability that may not be predictable, particularly the ISO (e.g. Shao et al., 2015; Wang et al., 2018), which is itself subject to interannual variations relating to large-scale modes, such as the Pacific-Japan teleconnection (Li et al., 2014). The skill of the ensemble (0.5) is therefore marginally higher than using predicted ENSO conditions alone to predict monsoon onset, though both are skillful.
Figure 3a provides additional insight by showing the correlation between the ensemble-mean SCSSM onset dates for the 23 years from the hindcast and observed global monthly mean SSTs in March over the same period. This illustrates that the predictable part of the SCSSM onset from the hindcast is strongly correlated with an ENSO-like pattern of Pacific SSTs, consistent with the findings of (Zhu and Li, 2017). There is also a strong positive correlation with SSTs in the equatorial Indian Ocean, again indicating that warmer SSTs are associated with later SCSSM onset dates. For the observed onset dates derived from ERA-Interim (Fig. 3b), the correlations with SST are far smaller, due to the presence of additional factors in the observations that are not predicted by the ensemble mean. The average correlations between the SSTs and 1000 pseudo-time-series of SCSSM onset created by randomly choosing an individual ensemble member hindcast for each year (Fig. 3c) are naturally smaller than with the ensemble-mean time series, but not as low as those in observations (Fig. 3b). This suggests that some of the subseasonal variations (e.g., ISOs) that affect SCSSM onset in reality may not be sufficiently well represented by the model to capture such influences, even at the relatively high horizontal resolution used by GloSea5 (N216; about 60 km at 50°N). This is consistent with the findings of (Fang et al., 2016), who showed that while several aspects of the boreal summer ISO were improved in the Met Office Unified Model at this resolution, difficulty remained in realistic representation of the variance and propagation characteristics.
Figure3. Correlation coefficients between the SCSSM onset pentad derived using the (Wang et al., 2004) index and observed March average SSTs from HadISST1.1 for the period 1993-2015, using: (a) ensemble-mean onset dates from the hindcast; (b) onset dates from ERA-Interim; (c) 10 000 pseudo-time-series of onset dates created by randomly selecting an individual ensemble member from each year (panel shows average over all correlations). Contours and darker shades indicate correlations significant at the 1% (r=0.48) and 3% (r=0.40) levels respectively, for a one-tailed t-test.2
3.4. Robustness of SCSSM wind onset predictability to ensemble size
To assess the influence of ensemble size on the prediction skill using the (Wang et al., 2004) index, we randomly sample small ensembles of between 1 and 51 members from the 52 members in our combined ensembles with start dates between 17 March and 9 April, and recalculate the correlation between the ensemble-mean time series and that from the observations for different numbers of ensemble members. Figure 4 indicates that, for this measure of monsoon onset, the prediction skill (black line) rises quickly with ensemble size, reaching a mean value of 0.5 for a 28-member ensemble (which is the size of the standard operational hindcast set), and is robust (correlation coefficients averaged over all ensemble-mean time series are statistically significant at the 1% level for a one-tailed test) for around 10 ensemble members or more. This is a reflection of the strong and predictable influence of ENSO on wider tropical rainfall (Kumar et al., 2013; Scaife et al., 2017) and, here, on the SCSSM onset dates in the hindcast: in most of the summers following strong El Ni?o/La Ni?a years (e.g., 1998, 1999, 2000, 2001, 2005, 2008, 2010), the spread among ensemble members is small and several members identify the same onset pentad (see Fig. 1), thereby constraining the values selected by random sampling of the ensemble for those years.
Figure4. Effect of ensemble size on the skill of SCSSM onset predictions using the (Wang et al., 2004) index (solid line), denoted r (ens, obs), and the signal-to-noise ratio (correlation of ensemble-mean time series with a pseudo-time-series created by randomly selecting a single model ensemble member for each year; dashed line), denoted r (ens, mod). In both cases, for each choice of ensemble size, up to 10 000 ensemble-mean time series are generated by randomly selecting the chosen number of ensemble member onset dates (independently and without replacement) from the 52 onset dates diagnosed in each year in the combined ensemble and averaging over the chosen number of ensemble members. Dot-dashed lines indicate the values of r that are significant at the 1% and 0.1% levels for a one-tailed t-test.Several authors (e.g., Eade et al., 2014; Scaife et al., 2014; Dunstone et al., 2016) have demonstrated that the model's North Atlantic Oscillation is less predictable than that observed, so that a large number of ensemble members is required for good prediction skill. This was confirmed by repeatedly randomly selecting a single member to be the truth and using the ensemble mean of the remaining members to predict that member. In contrast, the dashed line in Fig. 4 indicates that the model's SCSSM onset dates are more predictable than those from reanalyses, i.e., that the model is over-confident in its predictions, as is often found for tropical rainfall (Weisheimer and Palmer, 2014). This again illustrates the dominant role of ENSO in providing the predictability in the model, while the observed onset dates are also influenced by intraseasonal variations that are unpredictable on the seasonal time scale.
In contrast, the (Gao et al., 2001) SCSSM onset index, which includes an increase of θ e in the SCS region as a measure of thermodynamic onset alongside the change to westerly winds, shows little predictability on seasonal time scales. We speculate that this is partly due to the region used by (Gao et al., 2001), as this includes the northern SCS, which can be influenced by incursions of cold air from the north. This additional influence is, like the ISO, inherently unpredictable on the seasonal time scale, and thus its inclusion through the northward extension of the box used for the (Gao et al., 2001) index compared with that of (Wang et al., 2004) is, in our view, a contributing factor in the reduced seasonal prediction skill. However, we propose that a seasonal forecast of the broadscale transition using the (Wang et al., 2004) index would provide some useful early information for forecasters, and their guidance could later be refined, using other measures such as the (Gao et al., 2001) index, with medium-range forecasts that may capture the influence of intraseasonal variations at shorter lead times.
(He and Zhu, 2015) investigated the correlations between the SCSSM onset [as determined by the (Wang et al., 2004) criteria] and the subsequent EASM rainfall from May to September in observations/reanalyses. They suggested that, in contrast with the traditional view that a later onset date would be associated with a lower-than-normal total seasonal rainfall amount, the region from the lower Yangtze River to Korea and southern Japan shows a positive correlation between the SCSSM onset date and the seasonal mean rainfall, i.e., early SCSSM onset tends to be followed by lower-than-normal seasonal-mean rainfall further north. (He and Zhu, 2015) associate this relationship with a persistent western North Pacific anticyclonic/cyclonic anomaly accompanied by decaying El Ni?o/La Ni?a conditions in boreal spring to summer (Wu et al., 2010; Stuecker et al., 2013; Hardiman et al., 2018). This suggests that skillful predictions of SCSSM onset could provide an indication of the seasonal-mean rainfall in parts of the EASM region.
To the best of our knowledge, this is the first time that skill in predicting the broadscale transition associated with the SCSSM onset on seasonal time scales in an operational dynamical forecasting system has been demonstrated. We encourage other centers to investigate this in their operational forecasting systems. While it is recognized that the onset and progression of the SCSSM and EASM systems is complex and may be influenced by other factors, such as synoptic events, intraseasonal variability and regional air-sea interactions with little or no predictability on the seasonal time scale, the ability to provide skillful predictions of whether the broadscale seasonal transition is likely to be early, late or normal provides useful, early information for local forecasters, particularly when combined with other predictions, such as the Yangtze River basin rainfall, which have also been shown to be skillful (Li et al., 2016) and are now provided in real time to the CMA (Bett et al., 2018).
