1. Introduction The East Asian-western North Pacific summer

monsoon (EA-WNPSM) is one of the largest fluctuations causing floods and droughts with various spatiotemporal scales. The monsoon system is categorized as the Changma (in Korea), Meiyu (in China), and Baiu (in Japan) (Kang et al., 1999; LinHo and Wang, 2002; Ha et al., 2005; Ha and Lee, 2007), with remarkable differences in their characteristics and rainy periods. The WNPSM is an oceanic and tropical monsoon system and their variation is out of phases with the EASM (Lau et al., 2000; Wu and Wang, 2000; Lee et al., 2014). The skillful prediction of the EA-WNPSM is an imperative but difficult task because of the prominent seasonal migration of the monsoon rainband from May to August (Tao and Chen, 1987) and complicated association with various processes (Wang et al., 2000, 2008b; Wu et al., 2009; among others). The previous studies have mentioned that the mechanisms to influence EA-WNPSM predictability and variability are recognized as the El Niño-Southern Oscillation (ENSO), Indian Ocean (IO), western Pacific (WP) thermal statuses (Chang et al., 1998; Wang et al., 2000; Lau et al., 2000; Lau and Wu, 2001; Yun et al., 2009; Xie et al., 2009), the North Atlantic Oscillation (NAO) (Wu et al., 2009), Tibetan Plateau warming via two distinct Rossby wave trains (Wu et al., 2007; Wang et al., 2008a), spring Eurasian snow cover (Yang and Xu, 1994; Yim et al., 2010), spring Arctic Oscillation (Gong et al., 2011), and the land-sea thermal contrast changes (Zhou and Zou, 2010) as well. The comparison of prediction between dynamical models’ multi-model ensemble and the physical-empirical (P-E) model was shown in Wang et al. (2015). They compared the prediction skills of the dynamical models’ multi-model ensemble (MME) and P-E model and concluded that the MME generated better results for the WNPSM rainfall, while the P-E model was better for predicting the EASM land rainfall. In this study, the statistical techniques can be used to find the predictability over the EA-WNPSM.

Chu et al. (2012) identified four major intraseasonal modes of the EA-WNPSM, classified by a self-organizing map (SOM) analysis. Oh and Ha (2015) (hereinafter referred to as OH15) discussed their distinct instabilities with dynamic-thermodynamic characteristics by underlying their regional features. Following previous studies (Chu et al., 2012; OH15), we now provide additional sources to predict the four intraseasonal modes of the EA-WNPSM, with the use of the P-E model. For a broader scope, we changed the names for the four modes from previous studies: Meiyu-Baiu, Changma, post-Changma, and dry-spell modes to pre-Meiyu & Baiu (SOM1), Changma & Meiyu (SOM2), WNPSM (SOM3), and Monsoon Gyre (SOM4) modes, respectively.

To address the physical processes that drive interannual changes with the ultimate goal of

assessing the predictability of EA-WNPSM, we attempt to use persistence and tendency precursors. The persistence signals for January-April (JFMA) and the tendency indicates from January-February to March-April (MA minus JF). To objectively and physically select consequential predictors, we focus on the SST/2m temperature over land (T2m) field, which reflect ocean and land surface anomalous conditions (Wang et al., 2015; Yim et al., 2015). The rest of this paper is organized as follows. Section 2 briefly explains the main ideas behind the implementation of SOM and the identification of the four major modes, section 3 describes their predictability, and section 4 summarizes and discusses the major results.

2. Data and methods 2.1 Observational datasets

The data used for the analysis of the geopotential height at 850 hPa and used for daily large-scale circulation fields as input datasets of the SOM were collected from 1979 to 2012 by the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis (Kalnay et al., 1996). The SST data for the period 1979–2012 were obtained from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) made available by the British Atmospheric Data Centre (BADC) (Rayner et al., 2003).

2.2 Extraction of four SOM modes

The SOM is a clustering algorithm and a type of unsupervised learning that is to classify the training data without any external information, which is related to feedforward networks (Kohonen, 1990). Following previous studies (Chu et al., 2012; OH15), we adjust their SOM analysis, by applying it to six daily dynamical monsoon indices used as input data. A detailed description of the input data is presented in Chu et al. (2012). Before selecting the four dominant SOM patterns, 3 by 3 maps were determined by Chu and Ha (2011). The 3 by 3 SOM patterns were chosen based on two criteria: the number of clustered days for each SOM node should be large enough to capture the observed daily fields and the number of SOM patterns should be small enough to describe the distinct intraseasonal patterns. The four modes account for more than 60% of the total frequency. A detailed description of the SOM can be found in OH15. Note that there are slight changes of the name for the four modes.

3. P r e d i c t a b i l i t y o f t h e d o m i n a n t intraseasonal modes

To improve the quality of prediction over the EA-WNPSM region, the four dominant modes are identified. With the aim of reducing social and economic losses, we predict the four intraseasonal modes by developing a physical understanding of the sources of predictability for the interannual variation

Predictability of four modes with distinguished intraseasonal phases of the East Asia-western North Pacific summer monsoon

Hyoeun Oh and Kyung-Ja Ha*

Pusan National University, Busan, Korea

(IAV) at each mode using persistence and tendency precursors. The IAV of the SOM patterns is represented as annual occurrences by counting the number of days in the year attributed to each pattern (OH15). To objectively and physically select consequential predictors, we focused on the SST/T2m, which reflect ocean and land surface anomalous conditions. Furthermore, the predictors were selected by avoiding those that were well correlated.

3.1 pre-Meiyu & Baiu (SOM1) mode

The pre-Meiyu & Baiu mode is characterized by an anomalous cyclonic circulation at the low-level and is associated with the mid-latitude system. In particular, the mode is associated with the vertical wind shear resulting from the strong meridional temperature gradient through thermal wind balance and relating to baroclinic instability. Considering its characteristics, a first predictor is defined as the clear meridional gradient between the western North Pacific (WNP) cooling and western Pacific (WP) warming signals of the MJ minus JF SST tendency field (Fig. 1b). The WNP and WP region are averaged over [120°-180°E, 20°-35°N] and [150°-180°E, 5°-20°N], respectively. The enhanced SST meridional gradient reflects its strong baroclinic instability, which is mainly responsible for the pre-Meiyu & Baiu mode. A second predictor is a persistent T2m warming over northeast Asia [120°-140°E, 45°-60°N] from JF to MA (Fig. 1a). Yim et al. (2010) investigated the warming tendency over the northeast Asia, and it foreshadowed the development of an anomalous cyclonic circulation in early summer, with a rainfall enhancement over East Asia. In the preceding winter, there was an eastern equatorial Pacific warming that disappeared rapidly

during the following spring. Thus, the eastern Pacific SST anomaly itself could not significantly influence this mode (Yim et al., 2015). Instead, the persistent Eastern IO SST warming, affected by the preceding wintertime ENSO, is a third predictor. The persistent warming signal over the eastern IO [100°-120°E, 30°-10°S] leads to warming over the maritime continent (Fig. 1a). The mode features that the relatively weak anticyclonic circulation anomaly causes an anomalous cyclonic circulation over the southern Japan through Rossby wave train propagating east and originating in the WNP.

3.2 Changma & Meiyu (SOM2) mode

The Changma & Meiyu mode largely results from the WNPSH, which causes strong tropic-subtropics connections and convective instability triggered by both cold dry air at the upper-level and warm moist air at the low-level (OH15). The Changma&Meiyu mode is strongly related to the preceding ENSO through the WNPSH corresponding to the tropical system (Wang et al., 2000). Both a warming in the IO and a weak cooling over the WNP forced by the preceding ENSO through the Walker circulation generate a Rossby wave train. As a result, an anomalous anticyclonic circulation occurs over the WNP region (Yun et al., 2008). The WNPSH plays an important role in predicting this mode. Thus, a first predictor is the persistent SST difference between the IO [50°-100°E, 20°S-5°N] and WP [125°-150°E, 0°-10°N] (Fig. 2a). A second predictor is the MJ minus JF tendency of SST over the Atlantic, which is related with a positive north Atlantic tripole (NAT) pattern (Fig. 2b). Only cooling signal is selected to ensure independent precursors [290°-300°E, 30°-40°N]. In addition, the cooling signal is highly correlated to the NAT pattern at the 99%

Figure 1 Predictors selection based on the correlation maps of the (a) anomalous January-February-March-April (JFMA) mean sea surface temperature (SST)/temperature at 2 m over land (T2m), (b) anomalous March–April (MA) minus January–February (JF) SST/T2m and SLP regarding the interannual variation (IAV) indicated by the annual number of days clustered in the pre-Meiyu & Baiu (SOM1) mode, and (c) predicted and cross-validated predictions for the pre-Meiyu & Baiu (SOM1) mode during 1979–2012. The cross-validation was done by removing three years around the predicted year. The black contour indicates the areas exceeding 95% confidence level for SST and T2m. The yellow boxes are the region of defining predictors.

confidence level with a correlation coefficient (CC) of -0.77. The NAT pattern is affected by the preceding winter NAO, with previous studies indicating that the NAO could support the EASM through several pathways. The NAO can excite westward propagating Rossby waves that affect the eastern Pacific trade winds and generate SST anomalies over the equatorial central Pacific, strengthening the WNPSH and then inducing the Changma and Meiyu rainbands (Wu et al., 2009; Wang et al., 2013). Furthermore, localized rainfall over the Korean Peninsula prevails in this mode so that we should also consider short temporal scales. For this reason, this mode can be influenced by the warming tendency between April and March over the Kuroshio region [130°-160°E, 25°-35°N] (Fig. 2c). The Kuroshio warming tendency may be concomitant with the strength anticyclonic circulation anomaly, indicating a link with the wind-driven circulation by air-sea interaction.

3.3 WNPSM (SOM3) mode

The WNPSM mode is an intermediate mode characterized by the pre-Meiyu & Baiu and Changma & Meiyu mode at the mid-latitude and has a strong cyclonic circulation anomaly over the WNP with intense precipitation and an anticyclonic circulation anomaly over southern Japan (OH15). A first predictor is associated with the persistent warming over the Tibetan Plateau and adjacent region [50°-80°E, 25°-40°N] (Fig. 3a), which changes downstream along the upper-level westerly jet stream enhancing the anomalous anticyclonic circulation over southeast of Japan (Wang et al., 2008a). A second predictor is the persistent eastern Pacific cooling [240°-280°E, 10°S-10°N] (Fig. 3a). An anomalous cyclonic circulation that is resultant from the decaying La Niña phase covers a large Pacific

Figure 3 Same as Fig. 1 but for the WNPSM (SOM3) mode.

Figure 2 Predictors selection based on the correlation maps of the (a) anomalous January-February-March-April (JFMA) mean sea surface temperature (SST)/temperature at 2 m over land (T2m) and SLP, (b) anomalous March–April (MA) minus January–February (JF) SST/T2m and SLP regarding the interannual variation (IAV) indicated by the annual number of days clustered in the Changma & Meiyu (SOM2) mode, (c) anomalous SST difference between April and March, and (d) predicted and cross-validated predictions for the Changma & Meiyu (SOM2) mode during 1979–2012. The cross-validation was done by removing three years around the predicted year. The black contour indicates the areas exceeding 95% confidence level for SST and T2m. The yellow boxes are the region of defining predictors.

area. A third predictor is defined by the warming tendency from JF to MA of the T2m averaged over the northeast Asia [110°-125°E, 55°-70°N] (Fig. 3b), reflecting reduced snow anomalies for that region (Yim et al., 2010). The third predictor is negatively and significantly correlated with the subtropical high variability by preventing the westward shift of the WNPSH during JJA. However, the mechanism of the warming tendency effects on the WNPSH remains unclear.

3.4 Monsoon Gyre (SOM4) mode

The Monsoon Gyre mode brings cold-dry conditions with the easterly flow over East Asia and represents suppressed precipitation over East Asia associated with a strong cyclonic circulation over the WNP (OH15). In addition, it is related with significant persistent SST signals over the tropical Atlantic and is affected by global predictors, including not only from the high-latitudes but also from the Southern Hemisphere. Even though this mode can be explained by the IO and Atlantic cooling, we focus on the Atlantic cooling as a first predictor, ruling out the IO cooling effect, since the two regions are not independent, i.e., a significant relationship between the IO and Atlantic cooling has been identified. Figure 4a shows that the decreasing SST over the tropical Atlantic [270°-330°E, 0-20°N]. The signal is used as a first predictor and plays an essential role in weakening the WNPSH, with opposite to the Changma & Meiyu mode. A second predictor is defined by the MA minus JF tendency of the averaged SST over the North Pacific [170°-210°E, 60°-70°N] (Fig. 4b), which induces the anomalous anticyclonic circulation over northeast Asia, maintaining its dry conditions. A third predictor is the warming SST tendency (MA minus JF means) averaged over the southern IO [50°-90°E, 35°S-15°N] (Fig. 4b). The three predictors are important for the generation of the cyclonic circulation anomaly over the WNP, which affects the Monsoon Gyre mode.

However, mechanisms have not been investigated yet and remain uncertain.

4. Summary In the present study, we investigated the sources of

predictability for the four dominant modes of the EA-WNPSM by understanding a physical factor using persistence and tendency precursors. To objectively and physically select the consequential predictors, we focused on the SST/T2m, based on the asymmetric response to the El Niño Southern Oscillation, ocean and land surface anomalous conditions. The physically meaningful and statistically robust predictors were selected based on the persistence from January to April (JFMA) and the tendency from January-February to March-April (MA minus JF). The cross-validated correlation skills between the predictions and IAV defined by the mean annual number of days clustered in each mode are significant. The cross-validated prediction results demonstrated that the four modes could be predicted with significant skills ranging from 0.58 to 0.71 in Figs. 1c-4c and Fig. 2d for the Changma & Meiyu mode. For the pre-Meiyu & Baiu mode, the meridional SST gradient tendency was the most important predictor since it could modulate the strong vertical wind shear linked to the baroclinic instability through thermal wind balance. Two other predictors were relevant to induce the associated circulation field of the pre-Meiyu & Baiu mode. There was a strong connection between the Changma & Meiyu mode and WNPSH, playing an important role in predicting the mode. The IO warming and WNP cooling through the Walker circulation generated a Rossby wave train. The difference between the IO and WNP SST was a main contributor for this mode. The NAT pattern was a second predictor and had a similar effect on the WNPSH variability. Additionally, we consider the short temporal scales associated with localized rainfall over the Korean Peninsula, which were relevant to the wind-driven circulation by air-sea interaction.

Figure 4 Same as Fig. 1, but for the Monsoon Gyre (SOM4) mode.

Precursors for the WNPSM and Monsoon Gyre modes, which occur in late summer, were more difficult to find. A first predictor for the WNPSM mode was associated with persistent warming over the Tibetan Plateau and adjacent region. The most significant predictor was the persistent SST cooling signal over the eastern Pacific. The anomalous cyclonic circulation covered a large area of the Pacific was the effect on a decaying La Niña phase. The Monsoon Gyre mode was mostly linked to the persistent cooling signal over the tropical Atlantic and associated with the weakened WNPSH anomaly induced by the two other predictors. We showed that the four intraseasonal modes can be predicted reasonably well by the statistical method. The results of this study have important implications for the improvement of the intraseasonal character in the EA-WNPSM prediction.

5. References Chu, J.-E. and K.-J. Ha, 2011: Classification of

intraseasonal oscillation in precipitation using self-organizing map for the East Asian summer monsoon. Atmos. Krn. Meteorol. Soc., 21, 221-228.

Chu, J.-E., N. H., Saji, and K.-J. Ha, 2012: Non-linear, intraseasonal phases of the East Asian summer monsoon: Extraction and analysis using self-organizing maps. J. Clim., 25(20), 6975-6988.

Gong, D.-Y., J. Yang, S.-J Kim, Y. Gao, D. Guo, T. Zhou, and M. Hu, 2011 : Spring Arctic Oscillation-East Asian summer monsoon connection through circulation changes over the western North Pacific. Clim. Dyn., 37, 2199–2216.

Ha, K.-J., S.-K., Park, and K.-Y. Kim, 2005: On interannual characteristics of climate prediction center merged analysis precipitation over the Korean peninsula during the summer monsoon season. Int. J. Clim. 25, 99-116.

Ha, K.-J. and S.-S. Lee, 2007: On the interannual variability of the Bonin high associated with the East Asian summer monsoon rain. Clim. Dyn. 28, 67-83.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Sana, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K.-C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, D. Joseph, 1996: The NCEP/NCAR 40-year reanalysis project. Bull Am. Meteorol. Soc. 77, 431–437.

Kang, I.-S., C.-H. Ho, and Y.-K. Lim, 1999: Principal Modes of Climatological Seasonal and Intraseasonal Variations of the Asian Summer Monsoon. Mon. Wea. Rev. 127, 322-340.

Kohonen, T., 1990: The self-organizing map. Proc. IEEE. 78, 1464-1480.

Lau, K.-M. and Wu HT, 2001: Principal Modes of Rainfall–SST Variability of the Asian Summer Monsoon: A Reassessment of the Monsoon–ENSO Relationship. J Clim 14: 2880–2895

Lee, E.-J, K.-J. Ha, and J.-G. Jhun, 2014: Interdecadal changes in interannual variability of the global monsoon precipitation and interrelationships among its subcomponents. Clim. Dyn. 42(9), 2585-2601.

Lin, H., and B. Wang, 2002: The time-space structure of the Asian-Pacific summer monsoon: A fast annual cycle view. J. Clim. 15, 2001–2019.

Michaelsen, J., 1987: Cross-validation in statistical climate forecast model. J. Clim. Appl. Meteorol. 26, 1589–1600.

Oh, H. and K.-J. Ha, 2015: Thermodynamic Characteristics and Responses to ENSO of Dominant Intraseasonal Modes in the East Asian Summer Monsoon. Clim. Dyn. 44(7), 1751-1766.

Rayner, N.-A., D.-E. Parker, E.-B. Horton, C.-K. Folland, L.-V. Alexander, D.-P. Rowell, E.-C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108(D14), 4407.

Tao, S. and L. Chen, 1987: A review of recent research on the East Asia summer monsoon in Chin, In: C.-P. Chang, T. N. Krishnamurti (eds) Monsoon Meteorology. Clarendon Press, pp. 60-92.

Wang, B., Q. Bao, B. Hoskins, G. Wu, and Y. Liu, 2008a: Tibetan Plateau warming and precipitation change in East Asia. Geophys. Res. Lett. 35, L14702.

Wang, B., Z. Wu, J. Li, J. Liu, C.-P. Chang, Y. Ding, and G.-X. Wu, 2008b: How to Measure the Strength of the East Asian Summer Monsoon? J. Clim. 21, 4449-4463.

Wang, B., B. Xiang, and J.-Y. Lee, 2013: Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions. PNAS 110, 2718-2722.

Wang, B., J.-Y. Lee, and B. Xiang, 2015: Asian summer monsoon rainfall predictability: A predictable m o d e a n a l y s i s . C l i m . D y n . d o i : 1 0 . 1 0 0 7 /s00382-014-2218-1.

Wu, R. and B. Wang, 2000: Interannual variability of summer monsoon onset over the western North Pacific and the underlying processes. J. Clim. 13, 2483–2501.

Wu, G., Y. Liu, Q. Zhang, A. Duan, T. Wang, R. Wan, X. Liu, W. Li, Z. Wang, and X. Liang, 2007: The influence of mechanical and thermal forcing by the Tibetan Plateau on Asian climate. J. Hydrometeorol. 8, 770– 789.

Wu, Z., B. Wang, J. Li, and F.-F. Jin, 2009: An empirical seasonal prediction model of the east Asian summer monsoon using ENSO and NAO. J. Geophys. Res. 144, D18120.

Xie, S.-P., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean Capacitor Effect on Indo–Western Pacific Climate during the Summer following El Niño. J. Clim. 22, 730–747.

Yang, S. and L. Xu, 1994: Linkage between Eurasian winter snow cover and regional Chinese summer rainfall. Int. J. Climatol. 14, 739–750.

Yim, S.-Y., J.-G. Jhun, R. Lu, and B. Wang, 2010: Two distinct patterns of spring Eurasian snow cover anomaly and their impacts on the East Asian summer monsoon. J. Geophys. Res. 115, D22113.

Yim, S.-Y., B. Wang, W. Xing, M.-M. Lu, 2015: Prediction of Meiyu rainfall in Taiwan by multi-lead physical-empirical models. Clim. Dyn., doi:10.1007/s00382-014-2340-0.

Yun, K.-S., B. Ren, K.-J. Ha, J.C.L. Chan, and J.-G. Jhun, 2009: The 30-60-day oscillation in the East Asian summer monsoon and its time-dependent association with the ENSO. Tellus 61A, 565-578.