Understanding of Interdecadal Changes in Variability and ...

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Nov 1, 2015 - Two dominant global-scale teleconnections—namely, western North Pacific–North American (WPNA) and circumglobal teleconnection ...
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Understanding of Interdecadal Changes in Variability and Predictability of the Northern Hemisphere Summer Tropical–Extratropical Teleconnection JUNE-YI LEE Research Center for Climate Sciences, Pusan National University, Busan, South Korea

KYUNG-JA HA Research Center for Climate Sciences, and Division of Earth Environmental System, Pusan National University, Busan, South Korea (Manuscript received 25 February 2015, in final form 2 August 2015) ABSTRACT Two dominant global-scale teleconnections—namely, western North Pacific–North American (WPNA) and circumglobal teleconnection (CGT)—in the Northern Hemisphere (NH) extratropics during boreal summer (June–August) have been identified as important sources for NH summer climate variability and predictability. An interdecadal shift in interannual variability and predictability of the WPNA and CGT that occurred around the late 1970s was investigated using reanalysis data and six coupled models’ retrospective forecast with a 1 May initial condition for the period 1960–79 (P1) and 1980–2005 (P2). The WPNA had a tight relationship with the decaying phase of El Niño–Southern Oscillation (ENSO) in P1, whereas it had a remarkably enhanced linkage with western North Pacific (WNP) summer monsoon rainfall in P2. The correlation coefficient between the WPNA and preceding ENSO (WNP monsoon rainfall) was reduced (increased) from 20.69 (0.1) in P1 to 20.60 (0.5) in P2. The CGT had a considerable connection with Indian summer monsoon rainfall (ISMR) in P1, whereas it had a strengthened relationship with the developing ENSO in P2. The correlation coefficient between the CGT and simultaneous ENSO (ISMR) was increased (decreased) from 20.41 (0.47) in P1 to 20.59 (0.24) in P2. Although dynamical models have difficulties in capturing the observed interdecadal changes, they are able to predict the interannual variation of the WPNA and CGT one month ahead, to some extent. The prediction skill of six models’ multimodel ensemble (MME) decreased (increased) from 0.78 (0.23) to 0.67 (0.67) for the WPNA (CGT) interannual variation. It is also noted that the spatial distribution of predictability and MME skill for 200-hPa geopotential height has been changed in relation to the changes in the WPNA and CGT.

1. Introduction Evidence has been emerging that the two dominant tropical–extratropical teleconnection modes in the Northern Hemisphere (NH) during boreal summer are significant sources of climate variability and predictability over the NH extratropics on intraseasonal (Ding and Wang 2007; Lee et al. 2013; Moon et al. 2013) and seasonal time scales (Ding and Wang 2005; Wang et al. 2009; Lee et al. 2010, 2011; Lee and Wang 2012; Ha et al. 2012; Jia et al. 2012). The western North Pacific– North American (WPNA) teleconnection consists of the

Corresponding author address: Dr. Kyung-Ja Ha, Division of Earth Environmental System, Pusan National University, Jangjeon-dong, Geumjeong-gu, Busan 609-735, South Korea. E-mail: [email protected] DOI: 10.1175/JCLI-D-15-0154.1 Ó 2015 American Meteorological Society

hemispheric uniform pattern largely driven by the decaying phase of El Niño–Southern Oscillation (ENSO) and the wave pattern in association with the western North Pacific (WNP) summer monsoon (WNPSM) rainfall anomaly (Ding et al. 2011). The circumglobal teleconnection (CGT) is characterized by zonally symmetric seesaw pattern that appears preferentially in summers preceding the peak phase of ENSO and the wave pattern in connection with the Indian summer monsoon (ISM) rainfall anomaly (Ding and Wang 2005). It is of critical importance to note that the two teleconnections have a global impact by linking convective heating anomalies over the Asian monsoon region with the NH circulation anomalies (Ding et al. 2011; Lee et al. 2011, 2014). The two teleconnections are well reproduced by the state-of-the-art coupled models. However, coupled

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models have a striking deficiency in capturing the relationship between the WPNA and WNPSM rainfall, although they are capable of replicating the CGT and ISM rainfall relationship (Lee et al. 2014). Lee et al. (2011) revealed that seasonal prediction skill for the boreal summer NH upper-level circulation mainly comes from the numerical models’ ability to predict the WPNA and CGT patterns, and that coupled models are able to predict both the spatial and temporal characteristics of the two patterns one month ahead with high fidelity, although they have difficulty in capturing details of the prominent wavelike structures of the observed CGT. Using predictable mode analysis (PMA), which is an integral approach in combining empirical analysis, physical interpretation, and retrospective predictions (Lee et al. 2011; Wang et al. 2015), it was estimated that the two modes together account for more than 85% of the variance of 200-hPa geopotential height variability over the tropics and about 35% over the midlatitudes (Lee et al. 2011). Given the fact that the two teleconnections are intimately linked to seasonal climate variability in the NH, their interdecadal change should have a significant climate impact. Wang et al. (2012) demonstrated that the CGT has experienced a significant interdecadal change since the late 1970s as a consequence of 1) weakened coupling between the ISM rainfall and the midlatitude circulation associated with changes in ENSO properties and 2) the southward shift of upperlevel westerlies over the North Atlantic and Europe that tends to enhance coupling between the West African summer monsoon rainfall and midlatitude circulation. In particular, changes in ENSO characteristics since the late 1970s, including frequency, intensity, structure, and propagation (Wang 1995; Gu and Philander 1997; Wallace et al. 1998; An and Wang 2000), have played an important role in changes of the interannual variability of the ENSO–Asian summer monsoon relationship (Wang et al. 2008), the NH atmospheric variability and predictability (Jia et al. 2014a,b), and Asian winter monsoon variability (Yun et al. 2014). Tang et al. (2008) showed that predictability and prediction skill are higher after the 1980s, when the ENSO signal strengthens and the degree of asymmetry is enhanced compared to the period 1900–60. Jia et al. (2014a,b) also found that predictability and prediction skill of NH circulation and surface climate anomalies are higher for 1980–2005 than those for 1960–79. A natural question arises as to whether the WPNA has changed because of the global climate regime shift. Thus, the present study strives to understand how the interdecadal shift that occurred around the late 1970s

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modulates interannual variability and predictability of the two teleconnection patterns using observation and six coupled models’ retrospective forecast for the 46 years of 1960–2005. Particular attention is paid to understand 1) interdecadal change in potential predictability and seasonal prediction skill by comparing before the tropical Pacific climate shift (1960–79, P1) and after the shift (1980–2005, P2) and 2) physical mechanisms underlying the interdecadal changes. The WPNA (CGT) can be well represented by either the first (second) empirical orthogonal function (EOF) mode of NH 200-hPa geopotential height (Z200) or the second (first) maximum covariance analysis (MCA) mode of tropical precipitation and NH Z200 (Ding et al. 2011; Lee et al. 2011). This study applies the EOF analysis on the NH Z200 to identify the two teleconnections during P1 and P2. The paper is organized as follows: A detailed description of the models and data used are given in section 2. Section 3 examines the interdecadal change of interannual variability of the WPNA and CGT in observation. Interdecadal changes of predictability and prediction skill of the two teleconnections are discussed in section 4. The last section summarizes the major results of this study.

2. Models and data The retrospective forecast data for the 46 years of 1960–2005 used in this study were obtained from six coupled models. Table 1 lists the acronyms of the institutions, models, and projects mentioned in the text. The six coupled models are the CAWCR from the APCC/CliPAS (Wang et al. 2009; Lee et al. 2010) and the CMCC-INGV, ECMWF, IFM-GEOMAR, MF, and UKMO from the ENSEMBLES project (Weisheimer et al. 2009; Alessandri et al. 2011). Refer to Table 1 for the full name of each acronym used in the text (Jia et al. 2014a). Table 2 presents a summary of the coupled models and their retrospective forecasts with a 1 May initial condition targeting June–August (JJA) seasonal forecast. The multimodel ensemble (MME) is constructed using equal weights to all six models. Each model has a different atmospheric and oceanic initial condition. Details can be found in Weisheimer et al. (2009). The observation data used for the period 1960–2005 are as follows: 1) the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NCEP-1) data (Kalnay et al. 1996) for Z200 and 200-hPa zonal wind (U200); 2) twentieth-century merged statistical analyses of historical monthly precipitation anomalies reconstructed globally (Smith et al. 2010); and 3) the

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TABLE 1. Acronyms and their corresponding expansions for the institutions and models used in the text. Acronym

Full name

APCC CAWCR CliPAS CMCC-INGV ECMWF ENSEMBLES IFM-GEOMAR MF POAMA UKMO

Asia-Pacific Economic Cooperation (APEC) Climate Center Centre for Australian Weather and Climate Research Climate Prediction and its Application to Society Centro Euro-Mediterraneo per I Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia European Centre for Medium-Range Weather Forecasts Ensemble-Based Predictions of Climate Changes and Their Impacts Leibniz Institute of Marine Sciences at Kiel University–Research Center for Marine Geosciences Météo-France Predictive Ocean Atmosphere Model for Australia Met Office

improved Extended Reconstructed Sea Surface Temperature (SST), version 2 (ERSST.v2) data (Smith et al. 2008). Results obtained from NCEP-1 are validated against those from the combined datasets from ERA-40 for 1958–79 (Uppala et al. 2005) and ECMWF interim reanalysis (ERA-Interim; Dee et al. 2011) for 1979–2005 and those from the Twentieth Century Reanalysis (20CR) data. All observed and forecast data were interpolated to a 2.58 latitude 3 2.58 longitude grid.

3. Changes in mean and interannual variability This section examines the observed changes in background status and interannual variability of the first two EOF modes of JJA Z200 in the NH from P1 to P2 and how the current coupled models capture the changes.

a. Mean and total variance Changes in climatological mean and total variability of Z200, SST, and precipitation from P1 to P2 are first investigated. In general, large increase in climatological mean of Z200 is observed over most regions of the NH except the extratropical North Pacific (Fig. 1a) in association with the pattern of global SST changes from P1 to P2 (Fig. 1b). The SST difference (Fig. 1b) indicates the phase transition from negative to positive in the Pacific decadal oscillation (PDO) and from positive to negative in the Atlantic multidecadal oscillation (AMO) from P1 to P2. The changes in global thermal status lead to a southward shift of the jet stream over Eurasia and East Asia on the one hand and intensification of the jet stream over North America and the North Atlantic on the other hand (not shown). The changes in jet stream should play a critical role in changes to circumglobal teleconnection as a waveguide (Branstator 2002). Wang

TABLE 2. Description of the coupled models with their AGCM and OGCM, including horizontal and vertical resolutions, and their retrospective forecast used in this study. (Expansions of acronyms are available at http://www.ametsoc.org/PubsAcronymList.)

Institute

Model name

AGCM

OGCM

CAWCR

POAMA2.4

ACOM3, 0.58–1.58 lat 3 2.08 lon, L31

CMCC-INGV

CMCC

Bureau of Meteorology Research Centre Atmospheric Model, version 3d (BAM 3.0d), T47, L17 ECHAM5, T63, L19

ECMWF

ECMWF

IFS Cycle 31R1, T159, L62

IFM-GEOMAR

IFM

ECHAM4, T42, L19

MF

MF

IFS, T95, L40

UKMO

UKMO

HadGEM2-A, N96, L38

Ensemble members 10

OPA 8.2, 2.08 3 2.08, L31

9

HOPE using Arakawa E grid (HOPE-E), 1.48 3 0.38–1.48, L29 OPA 8.2, 2.08 lat 3 2.08 lon, L31

9

OPA 8.0, 182 3 152 grid points, L31 HadGEM2 ocean model (HadGEM2-O), 0.338–18, L20

9 9 9

Reference Zhong et al. (2005)

Alessandri et al. (2011); Pietro and Masina (2009) Stockdale et al. (2011); Balmaseda et al. (2008) Keenlyside et al. (2005); Jungclaus et al. (2006) Daget et al. (2009); Salas Mélia (2002) Collins et al. (2008)

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parts of the ISM region, but there is a significant increase over the WNPSM and East Asian monsoon (EAM) regions.

b. The WPNA mode

FIG. 1. Changes in climatology (shading) and variance (contour) of JJA (a) Z200, (b) SST, and (c) precipitation (PRCP) for the period 1980–2005 relative to those for the period 1960–79. The contour levels are 2500, 2200, 250, 50, 200, and 500 m2 for Z200; 20.48, 20.28, 20.18, 0.18, 0.28, and 0.48C2 for SST; and 20.8, 20.4, 20.1, 0.1, 0.4, and 0.8 (mm day21)2 for PRCP. In (a), the climatology of 200-hPa zonal wind (thick black contour) for the period 1980– 2005 is superimposed. The contour levels are 20 and 30 m s21.

et al. (2012) suggested that the southward shift of the jet stream over Eurasia and East Asia is responsible for the weakening of variability of the CGT centers, in particular over west of the Tibetan Plateau and East Asia region, which is also depicted in Fig. 1a. Figure 1a further suggests that the intensification of the jet stream over North America and the North Atlantic may play a role in the considerable intensification of Z200 variability over the two regions in P2. On average, the total variability of Z200 over the entire NH is increased by 4.7% in P2 relative to that in P1. Precipitation change from P1 to P2 is characterized by a large increase over the equatorial Pacific, the western Indian Ocean, and the East Asia–Kuroshio region, but it decreases over the eastern Indian Ocean, the Maritime Continent, the equatorial Atlantic, and the subtropical North and South Pacific (Fig. 1c). There is a slight decrease in precipitation variability over some

The percentage variance explained by the WPNA mode has been slightly reduced from 36.5% in P1 to 34.4% in P2 (Fig. 2). Since the variance of total Z200 variability over the entire NH has been slightly increased by 4.7% after the late 1970s, the total variance of the WPNA has been decreased by 1%, which is a very subtle change. The noteworthy changes from P1 to P2 appear in the spatial distribution of the WPNA, mostly in its wave pattern over the Asia–Pacific–Americas sector (Fig. 3). In particular, the associated precipitation pattern with the WPNA has been substantially strengthened over the WNP and East Asia from P1 to P2, signifying a noticeable enhancement between the WPNA wave pattern and the WNPSM rainfall relationship during recent decades. On the other hand, dominant precipitation anomalies associated with the WPNA were seen in the equatorial Pacific during P1, suggesting the decaying phase of ENSO played a more important role in the WPNA teleconnection pattern in the previous epoch. The six coupled models and their MME tend to overestimate the percentage variance explained by the WPNA and exhibit enhancement of the WPNA’s variance from P1 to P2 differently from observation. The MME reproduces well the hemispheric uniform pattern of the WPNA with the pattern correlation coefficient (PCC) skill of 0.89 and 0.87 for P1 and P2, respectively, but it cannot simulate its wave pattern mainly because of its deficiency in capturing the mean and variability of the WNPSM rainfall as discussed in previous studies (Lee et al. 2010, 2014). The one-month lead MME forecast skill for the interannual variation of the WPNA has been slightly decreased during the recent epoch. The temporal correlation coefficient (TCC) skill for the principal component (PC) time variation is 0.78 and 0.67 during P1 and P2, respectively.

c. The CGT mode Two major changes in the CGT are identified. First, the percentage variance explained by the CGT has been noticeably enhanced from 9.8% in P1 to 14.8% in P2 (Fig. 2) and the total variance of the CGT has increased by 34%. The change in total variance of the CGT is much larger than that of the WPNA. Second, major centers of the CGT wave pattern and their strength have been considerably changed (Fig. 4a). During P1, the wavelike pattern around 408N exhibits strong positive variability over southern Europe, East

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FIG. 2. Percentage variance (%) explained by the first seven EOF modes of the observed Z200 anomalies during boreal summer in the NH (equator–808N) for the period (a) 1960–79 and (b) 1980–2005. The bars represent one standard deviation of the sampling errors based on the method by North et al. (1982).

Asia, North Pacific, and the western part of North America, associated with monsoon rainfall anomalies over the ISM, WNP, and western African monsoon regions. On the other hand, during P2, the CGT wave pattern is weakened but its zonally symmetric seesaw pattern between the tropics and extratropics with a node along around 208N is reinforced as depicted by Wang et al. (2012). The precipitation pattern associated with the CGT has been changed considerably as well (Fig. 4a). During P1, the CGT is highly connected with the ISM rainfall anomalies. The anticyclone anomaly over west of the Tibetan Plateau (known as the South Asian high) and over East Asia are known to be driven by ISM rainfall (Ding and Wang 2005). On the contrary, the associated ISM rainfall anomaly is notably weakened in P2. Instead, enhanced rainfall over the Maritime Continent and suppressed rainfall over the equatorial Pacific are noticeable. The dipole pattern is a signature of a developing La Niña. Figure 4b indicates that the MME has difficulty in capturing the major centers of the CGT wave pattern as discussed in previous studies (Lee et al. 2011, 2014) and that it significantly overestimates the percentage variance explained by the second EOF mode during both periods compared to observation. While the spatial distribution of the regressed precipitation anomalies in observation exhibits considerable changes, the MME produced significant intensification in the associated precipitation anomalies without changes in spatial distribution. In spite of the considerable deficiency in model performance, it is interesting to note that the MME has much better skill for the spatial distribution

and time variation of the CGT in P2 than in P1. Not only is the PCC skill for capturing the EOF spatial distribution increased from 0.63 in P1 to 0.78 in P2, but also the TCC for predicting interannual variation of the CGT is considerably improved from 0.23 in P1 to 0.67 in P2.

4. Possible causes for the changes a. The WPNA mode It is suggested from Fig. 3 that interdecadal changes in the WPNA should be linked to changes in the WPNA’s association with the decaying phase of ENSO as well as WNPSM rainfall. Here, we investigate possible reasons for the change in details. We first calculate the TCC between the first PC (PC1) and seasonal mean SST anomaly from the preceding boreal winter to the concurrent summer during P1 and P2, respectively, shown in Fig. 5. There are three prominent changes in the global SST and the WPNA connection after the late 1970s. First, the WPNA’s relationship with the decaying phase of ENSO is largely weakened during P2 than during P1. The negative correlation over the central and eastern tropical Pacific appears during the preceding December–February (DJF), indicating that a strong positive (negative) phase of the WPNA tends to occur during the summers after the mature phase of La Niña (El Niño). Table 3 further indicates that the TCC between the WPNA and the Niño-3.4 during the previous winter is reduced from 20.69 for P1 to 20.6 for P2. In addition, the WPNA’s association with the

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FIG. 3. Spatial patterns of the first EOF eigenvector of Z200 anomalies (contour) and the regressed JJA precipitation anomalies (shaded) against the corresponding PC obtained from (a) observation and (b) the MME for the periods 1960–79 and 1980–2005. Stippling denotes areas where the magnitude of regressed rainfall exceeds a statistical significance test at the 95% confidence level. (c) The PCs of the first EOF mode obtained from observation (black solid line) and the MME (red dashed line) for the periods 1960–79 and 1980–2005. In (a),(b), the contour levels are 22, 21.5, 21, 20.5, 0, 0.5, 1, 1.5, 2, and 2.5 (nondimensional). The numbers in the top-left corner of the panels in (b) indicate the PCC between observation and the corresponding prediction. The TCC between the observed and predicted PC is also given at the bottom of the panel in (c).

concurrent central Pacific SST anomaly measured by the Niño-4 index is significantly decreased from 20.45 for P1 to 20.1 for P2 at the 99% confidence level using a z test. Second, the tripolar SST pattern in the North Atlantic from the previous spring and the simultaneous summer is highly related to the WPNA just during P1. It has been shown that the tripolar pattern was induced by spring North Atlantic Oscillation (NAO) and then persisted during summer probably as a result of the ocean memory effect (Wu et al. 2009). It, in turn, excites downstream development of subpolar teleconnections across

northern Eurasia, leading to the modulation of the WNPSM circulation (Wu et al. 2009). Yim et al. (2014) demonstrated that the linkage between the NAO and WNPSM was weakened during the period 1979–94 but strengthened during the period 1995–2010. The interdecadal change is likely reflected in the weakened connection between the tripolar SST pattern and the WPNA during P2, and the connection during P2 is weaker than that during P1. Last, the Indo-Pacific SST is more tightly correlated with the WPNA during P2 than during P1 attributable

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FIG. 4. As in Fig. 3, but for the second EOF mode.

to the strengthened connection between ENSO and the Indian Ocean basinwide mode since the late 1970s (Xie et al. 2010; Chowdary et al. 2012). The change suggests that the enhancement of the Indo-Pacific SST coupling with the WNP anticyclone circulation (WNPAC) in P2 is the major mechanism of the WNPAC and WNPSM variability (Wang et al. 2013; Kosaka et al. 2013). The dipole SST pattern characterized by cold (warm) IndoPacific Ocean and warm (cold) North Pacific Ocean accompanied by the positive (negative) phase of WPNA can sustain and strengthen the cyclonic (anticyclonic) circulation anomaly originally driven by La Niña (El Niño) forcing that weakens (strengthens) WNPAC. The weakening of WNPAC is accompanied by enhancement of WNPSM rainfall and vice versa (Wang et al. 2013).

The relationship of the WPNA with WNPSM rainfall (WNPSMR) and the WNPSM circulation index (WNPSMI) suggested by Wang and Fan (1999) is investigated further. The WNPSMR is obtained by averaging rainfall over the region 7.58–22.58N, 127.58E–1808, and WNPSMI is defined by differences between the 850-hPa zonal wind averaged over the southern part of the WNP region (58–158N, 1108–1308E) and that over the northern part (208–308N, 1108–1408E). The TCC between the PC1 and WNPSMR (WNPSMI) is significantly enhanced from 0.10 (0.02) for P1 to 0.50 (0.46) for P2 (Table 3) at the 99% confidence level in a z test. The significant intensification of the WPNA and WNPSMR relationship is attributable to the aforementioned enhancement in the air–sea process between the IndoPacific SST dipole and WNPAC.

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FIG. 5. Spatial patterns of the TCCs between the first PC and seasonal mean SST anomaly in the (top) preceding winter, (middle) spring, and (bottom) simultaneous summer for the periods (a) 1960–79 and (b) 1980–2005, obtained from observation. Correlation coefficients that are statistically significant at the 95% confidence level are contoured. The 21 and 0 in parentheses in the panel subtitles indicate the previous and concurrent years with the EOF1 mode variation, respectively.

To sum, the relationship between the WPNA and the preceding ENSO has been weakened, to a degree, but the WPNA’s linkage with WNPAC and WNPSMR variability has been significantly enhanced after the late 1970s. The decrease in the MME TCC skill for predicting the interannual variation of WPNA (from 0.78 in P1 to 0.67 in P2) may be attributable to the weakened relationship between the WPNA and ENSO because ENSO is the major predictability source in the current coupled models.

b. The CGT mode Using numerical experiments, Ding and Wang (2005) demonstrated that the ISM rainfall anomaly without the developing ENSO forcing can drive the significant CGT wave pattern. On the other hand, the developing ENSO forcing alone without the ISM rainfall anomaly cannot generate the significant CGT wave pattern but can drive the zonally symmetric seesaw pattern. Figure 4 suggests the CGT’s relationship with the developing ENSO and ISM rainfall variability has experienced significant changes. The regressed precipitation anomalies indicate

that the positive (negative) phase of the CGT is more tightly associated with the enhanced (reduced) ISM rainfall anomalies in P1 than in P2, whereas it is better correlated with the dipole pattern of enhanced (suppressed) rainfall over the Maritime Continent and suppressed (enhanced) rainfall over the equatorial Pacific in

TABLE 3. The TCC for PC1 and PC2, with El Niño indices and monsoon rainfall and circulation indices. The boldface values indicate that the TCC is statistically significant at the 99% confidence level. Refer to the text for the definition of each index. Mode

Index

1960–79

1980–2005

PC1 (the WPNA mode)

D(21)JF(0) Niño-3.4 JJA(0) Niño-4 JJA(0) Niño-3 JJA(0) WNPSMR JJA(0) WNPSMI JJA(0) Niño-3.4 JJA(0) Niño-4 JJA(0) Niño-3 JJA(0) ISMR JJA(0) ISMI

20.69 20.45 20.01 0.10 0.02 20.41 20.37 20.56 0.47 0.42

20.60 20.1 20.28 0.50 0.46 20.59 20.50 20.56 0.24 0.19

PC2 (the CGT mode)

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FIG. 6. Spatial patterns of the TCCs between the second PC and seasonal mean SST anomaly in (top) preceding spring, (middle) simultaneous summer, and (bottom) following fall for the periods (a) 1960–79 and (b) 1980–2005, obtained from observation. Correlation coefficients that are statistically significant at the 95% confidence level are contoured. The 0 in parentheses in the panel subtitles indicates the concurrent year with the EOF2 mode variation.

P2 than in P1. The dipole pattern is a signature of developing ENSO. Figure 6 and Table 3 clearly show that the developing ENSO should play a more critical role in the CGT in P2 than in P1, possibly enhancing the total variance of the CGT. The TCC between the CGT and simultaneous Niño-3.4 index (Niño-4 index) is increased from 20.41 (20.37) for P1 to 20.59 (20.50) for P2. It is important to note the change in ENSO evolution. In P2, the associated ENSO with the CGT already develops in the previous spring and thus has significant amplitude of its anomaly during summer. However, ENSO tends to start during summer with a slightly weak signal over the central equatorial Pacific in P1. ENSO is the major source of climate predictability and prediction skill in the state-of-the-art coupled model (e.g., Wang et al. 2009). Thus, the significant increase in the ENSO–CGT correlation implies that the CGT is more predictable in P2. Figure 4c shows that the MME indeed has much better skill in predicting the interannual variation of CGT in P2 (0.67) than in P1 (0.23) at the 99% confidence level in a z test.

It is further noted that the significantly weakened relationship of the CGT with ISM rainfall (ISMR) averaged over the region 7.58–32.58N, 67.58–87.58E and the ISM circulation index (ISMI) is defined by differences between the 850-hPa zonal wind averaged over the southern part (58–158N, 408–808E) and that over the northern part (208–308N, 608–908E) (Wang et al. 2004). The TCC between the CGT and ISMR (ISMI) is significantly decreased from 0.47 (0.42) for P1 to 0.25 (0.19) for P2 (Table 3) at the 95% confidence level in a z test. Thus, the weakening of the CGT wave pattern— particularly over west of the Tibetan Plateau and East Asia—is attributable to the considerable decrease in the CGT and ISMR relationship. To summarize, the zonally symmetric seesaw pattern of the CGT mode, together with its total variance and prediction skill, has been considerably enhanced because of the strengthened relationship with the developing phase of ENSO, whereas its wave pattern has been weakened with changes in the variability’s center owing to its weakened connection with ISM rainfall in P2. Attributable to its enhanced relationship with

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FIG. 7. The TCC skill for the 1-month lead JJA prediction of (top) Z200, (middle) SST, and (bottom) PRCP for the periods (a) 1960–79 and (b) 1980–2005, with a 1 May initial condition obtained from six coupled models’ MME. Solid (dashed) line represents the statistical significance of the TCC at the 99% (95%) confidence level. The numbers in the top-left corner of each panel indicate the averaged TCC over the entire domain.

ENSO, the MME has significantly better skill in predicting the interannual variation of the CGT in P2 (0.67) than in P1 (0.23) at the 99% confidence level in a z test.

5. Changes in predictability The two dominant teleconnection modes during boreal summer are significant sources of climate variability and predictability over the NH extratropics on a seasonal time scale (Lee et al. 2011). Thus, the interdecadal changes in the two modes can influence seasonal prediction and predictability of boreal summer surface climate and atmospheric circulation in the NH. In terms of interannual variation, the MME has a slightly decreased skill for the WPNA probably because of the weakened relationship with the decaying phase of ENSO but a considerably increased skill for the CGT attributable to the strengthened connection with the developing phase of ENSO after the late 1970s. Interdecadal changes in prediction skill and predictability for surface climate

and upper-level circulation from P1 to P2 were investigated further. Figure 7 shows the TCC between the observation and the 1-month lead prediction of MME for Z200, SST, and precipitation during P1 and P2. There are no significant changes in the averaged TCC skill in the NH (0.70 in P1 and 0.71 in P2 for Z200). However, considerable changes are found in the spatial distribution of the TCC for all variables. For Z200, the MME has increased skill over the entire tropics and some part of Europe, Asia, western North America, and the North Pacific in P2. The MME also shows higher skill for SST and precipitation over the tropical Pacific. The higher skill for precipitation over the WNPSM region in P2 may be related to the significant increase in total variance of the observed precipitation over the region shown in Fig. 1c. Seasonal predictability for Z200 is estimated by PMA, which is an integral approach combining empirical analysis, physical interpretation, and retrospective prediction (Lee et al. 2011, 2013; Wang et al. 2015). The PMA method assumes that a few leading EOF modes of

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FIG. 8. Spatial patterns of (a) potential predictability in terms of attainable TCC skill and (b) the MME’s actual TCC skill for JJA Z200 anomalies for the periods (left) 1960–79 and (right) 1980–2005. Solid (dashed) line represents the statistical significance of the TCC at the 99% (95%) confidence level. The numbers in the top-left corner of each panel indicate the averaged TCC skill over the NH tropics (T; equator–308N) and extratropics (E; 308–808N).

interannual variability (the first two modes of Z200 in this study) represent the climate signal, whereas the rest of the higher modes are largely unpredictable noise. The WPNA and CGT explain 46.4% and 49.2% of the total variance of JJA Z200 in the NH during P1 and P2, respectively. Thus, on average, there is a slight increase in the predictability of Z200 in the entire NH during boreal summer. It should be mentioned that the PMA method intrinsically underestimates the observed predictability resulting from ignorance of potential sources for predictability in the rest of higher modes. However, PMA has been demonstrated as an alternative approach for predictability estimation to the conventional approach using ensemble simulation that is totally model dependent (Wang et al. 2015). Figure 8a illustrates the spatial distribution of potential predictability and actual MME skill. Predictability is defined as the attainable TCC skill assuming the first two leading modes are perfectly predictable, whereas other higher modes cannot be. It is of importance to note that there are significant changes in the spatial distribution of potential predictability (Fig. 8a) and actual skill (Fig. 8b), although the changes in averaged skill are not

large. The increase in predictability from P1 to P2 is mainly found over the extratropics. The averaged attainable skill over the NH extratropics (308–808N) changed from 0.53 in P1 to 0.57 in P2. Consistently, the averaged actual MME skill over the region of interest increased from 0.27 in P1 to 0.35 in P2 (Fig. 8b), whereas no significant increase in predictability and prediction skill is found over the tropics.

6. Summary and discussion This study investigates the interdecadal change in variability and predictability of the two dominant boreal summer teleconnection modes occurring around the late 1970s using observation and six coupled models’ retrospective forecast with 1 May initial condition for the 46 years of 1960–2005. EOF analysis is applied on the interannual component of JJA Z200 anomalies to identify the two teleconnections during the 1960–79 (P1) and 1980– 2005 (P2) periods, respectively. The first (second) EOF mode of NH Z200 in boreal summer well represents the WPNA (CGT) mode as discussed in previous studies (e.g., Ding et al. 2011; Lee et al. 2011).

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FIG. 9. Schematic summarizing the changes of the WPNA and CGT mode from P1 to P2 in terms of spatial distribution, interannual change, and their relationship with monsoon precipitation and ENSO. The changes in the TCC (r) for the ENSO, monsoon precipitation, and MME skills are also displayed. A plus (minus) sign indicates a positive (negative) contribution to the teleconnection modes from each component.

It is found that the two dominant teleconnection patterns experienced a significant interdecadal shift in their interannual variability around the late 1970s as summarized in Fig. 9. Major changes in the WPNA mode from P1 to P2 are as follows. d

d

d

The total variance of the WPNA mode is not changed (e.g., 1% increase) and its percentage variance is slightly reduced from 36.5% to 34.4%. The hemispheric uniform pattern of the WPNA is weakened because of the enfeebled relationship with the decaying phase of ENSO, whereas its wave pattern are considerably enhanced owing to its strengthened connection with the WNPAC and WNPSM rainfall variability in P2. The TCC is decreased from 20.69 to 20.60 between the WPNA and the preceding ENSO but substantially increased from 0.1 to 0.5 between the WPNA and the WNPSM rainfall. Note that the relationship between the WNPSM rainfall and the decaying phase of ENSO is also significantly intensified. Regardless of the weakening of the WPNA–ENSO relationship, the WNPSM and ENSO relationship is significantly strengthened (e.g., Wang et al. 2008). There is a slight decrease in the MME forecast skill from 0.78 to 0.67 for the interannual variability of the WPNA mode attributable to its weakened relationship with ENSO.

The CGT changes from P1 to P2 are as follows. d

The percentage variance explained by the CGT is noticeably enhanced from 9.8% to 14.8%, and the total variance of the CGT is considerably increased by 34%.

d

d

The zonally symmetric seesaw pattern of the CGT is strengthened because of its enhanced linkage with the developing phase of ENSO, but its wave pattern is significantly weakened owing to the waning of its relationship with ISM rainfall variability. The TCC of the CGT with the simultaneous Niño-3.4 index and ISM rainfall is changed from 20.41 to 20.59 and from 0.42 to 0.24, respectively. Note that the relationship between the ISM rainfall and the simultaneous Niño-3.4 index is significantly weakened in P2 as indicated by previous studies (e.g., Kumar et al. 1999; Wang et al. 2012). The MME prediction with a 1 May initial condition has significantly better skill in P2 (0.67) than in P1 (0.23) mainly because of its enhanced connection with ENSO.

It is important to note that the changes from P1 to P2 are quite different from the future changes under anthropogenic global warming projected by the current coupled models (e.g., Lee et al. 2014; Lee and Wang 2014; Wang et al. 2014). Thus, the present study contributes to better understanding of the role of anthropogenic and natural forcing on the long-term teleconnection changes. The present study suggests that the Indo-Pacific air– sea coupling process associated with the decaying phase of ENSO plays a more crucial role in the WPNA in P2 than in P1. On the other hand, the tripolar SST pattern in the North Atlantic and the associated central Pacific warming or cooling contribute more to the WNP climate in P1. However, further study is necessary for better understanding of the physical process in those changes. Since the state-of-the-art coupled models considerably overestimate the ENSO effect but largely underestimate

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the impacts from other sources of predictability, ENSOrelated phenomena tend to be better predicted (e.g., Wang et al. 2009). Thus, the six models’ MME performs better in the WPNA during P2, but the CGT performs better during P1 when the teleconnection has better correlation with ENSO. This study demonstrates that the significant interdecadal shifts in the two teleconnection patterns occurred around the late 1970s should influence seasonal prediction and predictability of boreal summer atmospheric circulation in the NH. Regardless of the change in averaged predictability for Z200 over the NH, the spatial distribution of predictability experienced significant changes from P1 to P2. The result implies that better understanding of interdecadal changes in dominant climate modes is a prerequisite for improving seasonal climate prediction. Our recent study suggests that the two teleconnections each experienced another significant interdecadal shift around the middle-to-late 1990s that has important implications for recent seasonal climate prediction. The further study will address characteristics of the recent shifts and their impact to seasonal prediction for boreal summer climate in the NH. Acknowledgments. This work was supported by the Korean government (MEST) National Research Foundation of Korea (NRF) through a Global Research Laboratory (GRL) grant (MEST 2011-0021927). Authors also acknowledge support from the Brainpool project by the MEST NRF. We thank the ENSEMBLES project and the participant modeling groups listed in Table 1 for providing the prediction data. REFERENCES Alessandri, A., A. Borrelli, A. Navarra, A. Arribas, M. Déqué, P. Rogel, and A. Weisheimer, 2011: Evaluation of probabilistic quality and value of the ENSEMBLES multimodel seasonal forecasts: Comparison with DEMETER. Mon. Wea. Rev., 139, 581–607, doi:10.1175/2010MWR3417.1. An, S.-I., and B. Wang, 2000: Interdecadal change of the structure of the ENSO mode and its impact on the ENSO frequency. J. Climate, 13, 2044–2055, doi:10.1175/1520-0442(2000)013,2044: ICOTSO.2.0.CO;2. Balmaseda, M., A. Vidard, and D. L. T. Anderson, 2008: The ECMWF Ocean Analysis System: ORA-S3. Mon. Wea. Rev., 136, 3018–3034, doi:10.1175/2008MWR2433.1. Branstator, G., 2002: Circumglobal teleconnections, the jet stream waveguide, and the North Atlantic Oscillation. J. Climate, 15, 1893–1910, doi:10.1175/1520-0442(2002)015,1893: CTTJSW.2.0.CO;2. Chowdary, J. S., S.-P. Xie, H. Tokinaga, Y. M. Okumura, H. Kubota, N. Johnson, and X.-T. Zheng, 2012: Interdecadal variations in ENSO teleconnection to the Indo–western Pacific for 1870–2007. J. Climate, 25, 1722–1744, doi:10.1175/ JCLI-D-11-00070.1.

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Collins, W. J., and Coauthors, 2008: Evaluation of the HadGEM2 model, Hadley Centre Tech. Note 74, 47 pp. [Available online at http://www.metoffice.gov.uk/media/pdf/8/7/HCTN_74.pdf.] Daget, N., A. T. Weaver, and M. A. Balmaseda, 2009: Ensemble estimation of background-error variances in a three-dimensional variational data assimilation system for the global ocean, Quart. J. Roy. Meteor. Soc., 135, 1071–1094, doi:10.1002/ qj.412. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/ qj.828. Ding, Q., and B. Wang, 2005: Circumglobal teleconnection in the Northern Hemisphere summer. J. Climate, 18, 3483–3505, doi:10.1175/JCLI3473.1. ——, and ——, 2007: Intraseasonal interaction between the Eurasian wavetrain and the Indian summer monsoon. J. Climate, 20, 3751–3767, doi:10.1175/JCLI4221.1. ——, ——, J. M. Wallace, and G. Branstator, 2011: Tropical– extratropical teleconnections in boreal summer: Observed interannual variability. J. Climate, 24, 1878–1896, doi:10.1175/ 2011JCLI3621.1. Gu, D., and S. Philander, 1997: Interdecadal climate fluctuations that depend on exchanges between the tropics and extratropics. Science, 275, 805–807, doi:10.1126/science.275.5301.805. Ha, K.-J., J.-E. Chu, J.-Y. Lee, B. Wang, S. N. Hameed, and M. Watanabe, 2012: What causes the cool summer over northern Central Asia, East Asia, and central North America during 2009? Environ. Res. Lett., 7, 044015, doi:10.1088/ 1748-9326/7/4/044015. Jia, X., H. Lin, J.-Y. Lee, and B. Wang, 2012: Season-dependent forecast skill of the dominant atmospheric circulation patterns over the Pacific and North American region. J. Climate, 25, 7248–7265, doi:10.1175/JCLI-D-11-00522.1. ——, J.-Y. Lee, H. Lin, A. Alessandri, and K.-J. Ha, 2014a: Interdecadal change in the Northern Hemisphere seasonal climate prediction: Part I. The leading forced mode of atmospheric circulation. Climate Dyn., 43, 1595–1609, doi:10.1007/ s00382-013-1988-1. ——, ——, ——, H. Hendon, and K.-J. Ha, 2014b: Interdecadal change in the Northern Hemisphere seasonal climate prediction: Part II. Predictability and prediction skill. Climate Dyn., 43, 1611–1630, doi:10.1007/s00382-014-2084-x. Jungclaus, J. H., and Coauthors, 2006: Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J. Climate, 19, 3952–3972, doi:10.1175/JCLI3827.1. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, doi:10.1175/1520-0477(1996)077,0437:TNYRP.2.0.CO;2. Keenlyside, N., M. Latif, M. Botzet, J. Jungclaus, and U. Schulzweida, 2005: A coupled method for initializing El Niño Southern Oscillation forecasts using sea surface temperature. Tellus, 57A, 340–356, doi:10.1111/j.1600-0870.2005.00107.x. Kosaka, Y., S.-P. Xie, N.-C. Lau, and G. A. Vecchi, 2013: Origin of seasonal predictability for summer climate over the Northwestern Pacific. Proc. Natl. Acad. Sci. USA, 110, 7574–7579, doi:10.1073/pnas.1215582110. Kumar, K. K., B. Rajagopalan, and M. A. Cane, 1999: On the weakening relationship between the Indian monsoon and ENSO. Science, 284, 2156–2159, doi:10.1126/science.284.5423.2156. Lee, J.-Y., and B. Wang, 2012: Seasonal climate prediction and predictability of atmospheric circulation. Climate Models, L. M. Druyan, Ed., InTech, 19–42.

1 NOVEMBER 2015

LEE AND HA

——, and ——, 2014: Future change of global monsoon in the CMIP5. Climate Dyn., 42, 101–119, doi:10.1007/ s00382-012-1564-0. ——, and Coauthors, 2010: How are seasonal prediction skills related to models’ performance on mean state and annual cycle? Climate Dyn., 35, 267–283, doi:10.1007/s00382-010-0857-4. ——, B. Wang, Q. Ding, K.-J. Ha, J.-B. Ahn, A. Kumar, B. Stern, and O. Alves, 2011: How predictable is the Northern Hemisphere summer upper-tropospheric circulation? Climate Dyn., 37, 1189–1203, doi:10.1007/ s00382-010-0909-9. ——, ——, M. Wheeler, X. Fu, D. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493–509, doi:10.1007/s00382-012-1544-4. ——, ——, K.-H. Seo, J.-S. Kug, Y.-S. Choi, Y. Kosaka, and K.-J. Ha, 2014: Future change of Northern Hemisphere summer tropical–extratropical teleconnection in CMIP5 models. J. Climate, 27, 3643–3664, doi:10.1175/JCLI-D-13-00261.1. Moon, J.-Y., B. Wang, K.-J. Ha, and J.-Y. Lee, 2013: Teleconnections associated with Northern Hemisphere summer monsoon intraseasonal oscillation. Climate Dyn., 40, 2761– 2774, doi:10.1007/s00382-012-1394-0. North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 115, 27–50, doi:10.1175/ 1520-0493(1982)110,0699:SEITEO.2.0.CO;2. Pietro, P. D., and S. Masina, 2009: The CMCC-INGV Global Ocean Data Assimilation System (CIGODAS). CMCC Research Paper 71, 39 pp. [Available online at http://ssrn.com/ abstract51617954.] Salas Mélia, D., 2002: A global coupled sea ice–ocean model. Ocean Modell., 4, 137–172, doi:10.1016/S1463-5003(01)00015-4. Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land– ocean surface temperature analysis (1880–2006). J. Climate, 21, 2283–2296, doi:10.1175/2007JCLI2100.1. ——, P. A. Arkin, M. R. P. Sapiano, and C.-Y. Chang, 2010: Merged statistical analyses of historical monthly precipitation anomalies beginning 1900. J. Climate, 23, 5755–5770, doi:10.1175/2010JCLI3530.1. Stockdale, T., and Coauthors, 2011: ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Climate Dyn., 37, 344–471, doi:10.1007/s00382-010-0947-3. Tang, Y., Z. Deng, X. Zhou, Y. Cheng, and D. Chen, 2008: Interdecadal variation of ENSO predictability in multiple models. J. Climate, 21, 4811–4833, doi:10.1175/2008JCLI2193.1. Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/ qj.04.176. Wallace, J. M., E. M. Rasmusson, T. P. Mitchell, V. E. Kousky, E. S. Sarachik, and H. von Storch, 1998: On the structure and evolution of ENSO-related climate variability in the tropical Pacific: Lessons from TOGA. J. Geophys. Res., 103, 14 241– 14 259, doi:10.1029/97JC02905.

8647

Wang, B., 1995: Interdecadal changes in El Niño onset in the last four decades. J. Climate, 8, 267–258, doi:10.1175/ 1520-0442(1995)008,0267:ICIENO.2.0.CO;2. ——, and Z. Fan, 1999: Choice of South Asian summer monsoon indices. Bull. Amer. Meteor. Soc., 80, 629–638, doi:10.1175/ 1520-0477(1999)080,0629:COSASM.2.0.CO;2. ——, I.-S. Kang, and J.-Y. Lee, 2004: Ensemble simulations of Asian–Australian monsoon variability by 11 AGCMs. J. Climate, 17, 803–818, doi:10.1175/1520-0442(2004)017,0803: ESOAMV.2.0.CO;2. ——, J. Yang, T. Zhou, and B. Wang, 2008: Interdecadal changes in the major modes of Asian–Australian monsoon variability: Strengthening relationship with ENSO since the late 1970s. J. Climate, 21, 1771–1789, doi:10.1175/2007JCLI1981.1. ——, and Coauthors, 2009: Advance and prospectus of seasonal prediction: Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Climate Dyn., 33, 93–117, doi:10.1007/s00382-008-0460-0. ——, B. Xiang, and J.-Y. Lee, 2013: Subtropical High predictability establishes a promising way for monsoon and tropical storm predictions. Proc. Natl. Acad. Sci. USA, 110, 2718–2722, doi:10.1073/pnas.1214626110. ——, S.-Y. Yim, J.-Y. Lee, J. Liu, and K.-J. Ha, 2014: Future change of Asian-Australian monsoon under RCP 4.5 anthropogenic warming scenario. Climate Dyn., 42, 83–100, doi:10.1007/s00382-013-1769-x. ——, J.-Y. Lee, and B. Xiang, 2015: Asian summer monsoon rainfall predictability: A predictable mode analysis. Climate Dyn., 44, 61–74, doi:10.1007/s00382-014-2218-1. Wang, H., B. Wang, F. Huang, Q. Ding, and J.-Y. Lee, 2012: Interdecadal change of the boreal summer circumglobal teleconnection (1958–2010). Geophys. Res. Lett., 39, L12704, doi:10.1029/2012GL052371. Weisheimer, A., and Coauthors, 2009: ENSEMBLES: A new multimodel ensemble for seasonal-to-annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs. Geophys. Res. Lett., 36, L21711, doi:10.1029/2009GL040896. Wu, Z., B. Wang, J. Li, and F.-F. Jin, 2009: An empirical seasonal prediction of the east Asian summer monsoon using ENSO and NAO. J. Geophys. Res., 114, D18120, doi:10.1029/2009JD011733. Xie, S.-P., Y. Du, G. Huang, X.-T. Zheng, H. Tokinaga, K. Hu, and Q. Liu, 2010: Decadal shift in El Niño influences on Indo– western Pacific and East Asian climate in the 1970s. J. Climate, 23, 3352–3368, doi:10.1175/2010JCLI3429.1. Yim, S.-Y., B. Wang, and M.-H. Kwon, 2014: Interdecadal change of the controlling mechanisms for East Asian early summer rainfall variation around the mid-1990s. Climate Dyn., 42, 1325–1333, doi:10.1007/s00382-013-1760-6. Yun, K.-S., Y.-W. Seo, K.-J. Ha, J.-Y. Lee, and Y. Kajikawa, 2014: Interdecadal changes in the Asian winter monsoon variability and its relationship with ENSO and AO. Asia-Pac. J. Atmos. Sci., 50, 531–540, doi:10.1007/s13143-014-0042-5. Zhong, A., H. Hendon, and O. Alves, 2005: Indian Ocean variability and its association with ENSO in a global coupled model. J. Climate, 18, 3634–3649, doi:10.1175/JCLI3493.1.

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