Roles of Remote and Local Forcings in the Variation and Prediction ofRegional Maritime Continent Rainfall in Wet and Dry Seasons

TUANTUAN ZHANG

Department of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China

SONG YANG

Department of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change

and Natural Disaster Studies, and Institute of Earth Climate and Environment System, Sun Yat-sen University,

Guangzhou, Guangdong, China

XINGWEN JIANG

Institute of Plateau Meteorology, China Meteorological Administration, Chengdu, Sichuan, China

BOHUA HUANG

Center for Ocean–Land–Atmosphere Studies, and Department of Atmospheric, Oceanic, and Earth Sciences,

George Mason University, Fairfax, Virginia

(Manuscript received 4 June 2016, in final form 12 September 2016)

ABSTRACT

Seasonal prediction of extratropical climate (e.g., the East Asian climate) is partly dependent upon the

prediction skill for rainfall over theMaritimeContinent (MC). A previous study by the authors found that the

NCEP Climate Forecast System, version 2 (CFSv2), had difference in skill between predicting rainfall over

the western MC (WMC) and the eastern MC (EMC), especially in the wet season. In this study, the potential

mechanisms for this phenomenon are examined. It is shown that observationally in the wet season (from

boreal winter to early spring) the EMC rainfall is closely linked to both ENSO and local sea surface tem-

perature (SST) anomalies, whereas the WMC rainfall is only moderately correlated with ENSO. The model

hindcast unrealistically predicts the relationship of the WMC rainfall with local SST and ENSO (even op-

posite to the observed feature), which contributes to lower prediction skill for the WMC rainfall. In the dry

season (from boreal late summer to fall), the rainfall over the entire MC is significantly influenced by both

ENSO and local SST in observations and this feature is well captured by the CFSv2. Therefore, the hindcasts

do not show apparently different skill in rainfall prediction for EMCandWMC in the dry season. The possible

roles of atmospheric internal processes are also discussed.

1. Introduction

The Maritime Continent (MC) lies within the warmest

oceanic area, with heavy rainfall (Ramage 1968; Qian

2008), and it plays an important role in global atmospheric

circulation (Sun et al. 2009; Jiang et al. 2015). The sea-

sonal evolution of MC rainfall is characterized by a wet

season and a dry season (e.g., Murakami and Sumi 1982;

McBride et al. 2003; Hendon 2003; Chang et al. 2004; Jo

andAhn 2015). The variations ofMC rainfall, as well as its

relationship with ENSO and other atmospheric systems,

differ with seasons andwith areaswithin theMC (Nicholls

1981; Hastenrath 1987; Kiladis and Diaz 1989; Kirono

et al. 1999; Hamada et al. 2002). Previous studies have

demonstrated that the MC rainfall varies coherently and

is highly correlated with ENSO during the dry season

(e.g., Haylock and McBride 2001; McBride et al. 2003;

Hendon 2003; Chang et al. 2005b; Gomyo and Koichiro

Corresponding author address: Prof. Song Yang, Dept. of At-

mospheric Sciences, Sun Yat-sen University, 135 West Xingang

Road, Guangzhou 510275, China.

E-mail: yangsong3@mail.sysu.edu.cn

Denotes Open Access content.

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2009; Kubota et al. 2011). In the wet season, rainfall over

the MC (especially the western MC) is not highly corre-

lated with ENSO, which may be due to the spatial in-

coherence of rainfall (Haylock and McBride 2001), local

SST forcing that might cancel the ENSO influence

(Hendon 2003), or complicated wind–terrain interaction

during that season (Chang et al. 2004). Local and remote

forcings play different roles in driving the rainfall anom-

alies over theMC during different seasons (Nicholls 1979,

1984; Rowell 2001). However, it is unclear to what extent

these local and remote factors force the anomalies of re-

gional MC rainfall.

Several studies have reported that the prediction skill

for MC rainfall also varies among regions and seasons

(e.g., Neale and Slingo 2003; Kirono et al. 1999; Jiang

et al. 2013; Argueso et al. 2016). Within the MC, the

Indonesian wet-season rainfall is inherently unpredictable

(Haylock and McBride 2001). Multiple models have

shown much lower skill in predicting the MC rainfall

in the wet season compared to the dry season (e.g.,

Aldrian et al. 2007; Zhang et al. 2016). Over the

years, scientists have made a great deal of effort to

enhance representation of MC rainfall features in

climate models, but large biases still exist (e.g.,

Gianotti and Eltahir 2014a,b; Schiemann et al. 2014).

Moreover, few studies have paid attention to in-

vestigations of the factors that are responsible for the

difference in prediction skill of regional MC rainfall.

The aim of this study is to understand the respective

roles of remote and local forcings in the variation and

prediction of regional MC rainfall in wet and dry sea-

sons. Section 2 describes the model, data, and methods

applied. Results are shown in section 3. A summary of

the study and further discussion are provided in

section 4.

2. Model, data, and methods

The National Centers for Environmental Prediction

(NCEP)Climate Forecast System, version 2 (CFSv2), is a

fully coupled dynamical prediction system. It consists of

the NCEP Global Forecast System as its atmospheric

component; the Modular Ocean Model, version 4.0, as

the oceanic component; the NCEP, Oregon State Uni-

versity (OSU), Air Force Research Laboratory, and the

NOAA/Office of Hydrology land surface model (Noah);

and a three-layer interactive global sea ice model (Saha

et al. 2014). Output of rainfall, surface temperature [ST,

including sea surface temperature (SST) for oceans and

surface skin temperature for land], and 850-hPa winds

from the retrospective forecasts of 9-month integrations

by the CFSv2 from 1983 to 2010 are analyzed. Details

about the 9-month hindcast runs can be seen in Zhang

et al. (2016). We calculate the ensemble mean of the

24 members as in Zhang et al. (2016). For convenience,

the hindcast ensemble means at 0-month lead, 1-month

lead, and so on to a 9-month lead are denoted as LM0,

LM1, through LM9, respectively.

For model verification, rainfall from the Climate

Prediction Center (CPC) Merged Analysis of Rainfall

(CMAP) (Xie and Arkin 1997), SST from the National

Oceanic and Atmospheric Administration (NOAA)

Optimum Interpolation SST (OISST) (Reynolds et al.

2007), and 850-hPa winds from the NCEP Climate

Forecast System Reanalysis (Saha et al. 2010) are used.

Also, outgoing longwave radiation (OLR; daily mean)

from the NOAA Advanced Very High Resolution Ra-

diometer (AVHRR) (Liebmann and Smith 1996), and

925-hPa meridional wind (daily mean) from the NCEP–

DOEReanalysis-2 (Kanamitsu et al. 2002) are also used

in this study.

The relationships between MC precipitation and

other global and regional climate variations represented

by various climate indices are also examined. The Niño-3.4 index, defined as the SST averaged over the area

within 58S–58N and 1208–1708W, is used to represent

ENSO variability. The cold surge index analyzed is de-

fined as the following. A cold surge event occurs when

the average 925-hPa meridional wind between 1108 and117.58E along 158N exceeds 8ms21, following Chang

et al. (2005a), and the number of cold surge events in

boreal winter is calculated. The index for the Madden–

Julian oscillation (MJO) intensity is also defined. First

the OLR daily data were band-filtered with a 20–100-day

100-point Lanczos filter (see Slingo et al. 1999) and then

the variance of the filtered MC OLR was calculated

and averaged (for the wet season). In addition, a partial

correlation analysis was applied as in Zhang et al.

(2016). All datasets are analyzed for the period of 1983–

2010.

3. Results

Following Zhang et al. (2016), the dry season and the

wet season are defined as July–October and December–

March, respectively. As shown in Figs. 1a and 1b, the

CFSv2 shows high skill for the entire MC rainfall in the

dry season and lower skill over parts of the MC, espe-

cially New Guinea and the western MC (WMC) in the

wet season. Zhang et al. (2016) have reported a spatial

incoherency of SST–rainfall relationship in the wet

season, which shows a positive correlation over the

eastern MC (EMC) and a negative correlation over the

WMC. Thus, we conduct an analysis for the EMC (118S–108N, 1208–1458E) and WMC (138S–78N, 958–1208E)separately in this study. The EMC (WMC) rainfall index

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is defined as area-averaged rainfall over EMC (WMC).

Figures 1c and 1d show the variations in skill of rainfall

prediction as a function of calendar month and lead time

(in months) over the EMC (Fig. 1c) and the WMC

(Fig. 1d), respectively. The most noticeable feature in

Fig. 1c is that the correlation coefficients for the WMC

rainfall in July–November are above 0.6 and up to

5-month lead and that the correlation exceeding the

95% confidence level occurs at lead time longer than

8 months. However, the model’s skill is much lower in

December–June, with the correlation passing the 95%

confidence level only at 0-month lead. The prediction

FIG. 1. Correlations of rainfall (mmday21) between observation and CFSv2 in 1-month lead for the (a) wet

season and (b) dry season, and correlations between observation and CFSv2 predictions for area-averaged rainfall

for the (c) WMC and (d) EMC. Values exceeding the 90%, 95%, and 99% confidence levels are shaded. The

domains used to define WMC (138S–78N, 958–1208E) and EMC (118S–108N, 1208–1458E) are outlined with black

boxes in (a) and (b).

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skill for the EMC rainfall is also different between the

wet and dry seasons, but the difference is not as re-

markable as for theWMC. Themodel shows higher skill

in predicting theWMC rainfall than the EMC rainfall in

the dry season, but much lower skill in predicting the

WMC rainfall than the EMC rainfall in the wet season.

From the above analysis, one may ask the following

question: what factors are responsible for the differences

in the prediction skill between EMC and WMC?

a. Wet season

Variations of MC rainfall can be significantly mod-

ulated by ENSO and local SST (e.g., Hendon 2003;

Chang et al. 2004; Zhang et al. 2016); however, it is still

unclear how the two factors influence the rainfall var-

iations since local SST anomalies tend to occur in

conjunction with ENSO (Hendon 2003). As shown in

Figs. 2a and 2c, the variations of rainfall over both the

WMC and the EMC are significantly correlated with

ENSO. The spatial patterns of ST correlation and 850-hPa

wind regression with respect to WMC and EMC

rainfall anomalies are remarkably similar to each

other. However, the local ST–rainfall relationship is

very different. The EMC rainfall is positively correlated

with local ST, whereas negative correlation is seen be-

tween WMC rainfall and local ST. Besides, the in-

teraction between ENSO-related winds (anomalous

westerly winds over the Indian Ocean) and the terrain

over WMC, which can be seen from the difference be-

tween the ENSO-related and the anomalousWMC land

rainfall–related composite patterns of wind fields (fig-

ures not shown), leads to a more complicated relation-

ship between ENSO and the rainfall over this region,

consistent with study of Chang et al. (2004). This wind–

terrain interaction, which is difficult for the model to

capture, may result in lower prediction skill for the

ENSO–rainfall relationship over the WMC region.

When the ENSO influence is excluded, the WMC rain-

fall is uncorrelated with local ST while the EMC rain-

fall is still significantly correlated with the local ST

(Figs. 2b,d).

To investigate the possible influences of local and

remote forcings on EMC andWMC rainfall anomalies,

we analyze the correlation of preceding Niño-3.4

FIG. 2. Correlations (regressions) of observed ST (850-hPa winds) with (against) observed (a) WMC and

(c) EMC rainfall indices in the wet season. Shown also are partial correlations of observed ST (shading) and

850-hPa winds (vectors; m s21) with observed (b) WMC and (d) EMC rainfall indices in the wet season. Values

(shading) exceeding the 90%, 95%, and 99% confidence levels are shaded, and only the regressed winds (vectors)

exceeding the 90% confidence level are plotted. The WMC and EMC domains are outlined with black boxes in

(a),(b) and (c),(d), respectively.

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indices and local ST with the WMC and EMC rainfall

indices for the wet season (Table 1). While the WMC

rainfall is significantly related to previous central-

eastern Pacific SST (with lead time longer than

5 months), it is negatively correlated with local SST in

the wet season and insignificantly related to previous

local SST, suggesting that it is not forced by the local

ST. On the other hand, the EMC rainfall is significantly

correlated with both local SST and ENSO, also with

lead time longer than 5months. The influence of ENSO

on the EMC rainfall is slightly stronger than that on the

WMC rainfall.

Thus, the performance of CFSv2 in predicting the re-

lationship of rainfall anomalies with remote and local

forcings tends to have an impact on the skill of predicting

local rainfall variations. Then, we compare these re-

lationships in observations and the CFSv2. As an example,

the correlations between rainfall and ST anomalies in

January for observation and CFSv2 hindcasts are shown in

Figs. 3a–d. The most remarkable difference is that the

TABLE 1. Correlations of precedent Niño-3.4 indices and local area-averaged ST (leads by 0, 1, 3, and 5 months) with WMC and EMC

rainfall indices for wet and dry seasons. Values exceeding the 95% confidence level are shown in boldface.

0 month 1 month 3 months 5 months

Wet Dry Wet Dry Wet Dry Wet Dry

WMC–Niño-3.4 20.68 20.78 20.65 20.74 20.57 20.45 20.51 0.01

EMC–Niño-3.4 20.84 20.80 20.81 20.79 20.74 20.65 20.69 20.35

WMC–ST 20.27 0.66 20.08 0.55 0.20 0.39 0.20 0.30

EMC–ST 0.43 0.55 0.50 0.48 0.51 0.32 0.45 0.35

FIG. 3. Correlations between ST and rainfall in January for (a) observation, and hindcast ensemble mean of

(b) 0-month lead, (c) 1-month lead, and (d) 4-month lead. Coefficients of correlation between ST and rainfall over the

(e) WMC and (f) EMC in the wet-season months. R12, R1, R2, and R3 represent correlation coefficients in ob-

servation forDecember, January, February, andMarch, respectively. Black, red, green, and blue lines represent the

correlation coefficients in hindcast ensemblemean of different leads for December, January, February, andMarch,

respectively. Values that significantly exceed the 90%, 95%, and 99% confidence levels are shaded in (a)–(d).

Straight dotted line in (e) and (f) denotes the 90% confidence level. The domain of WMC and EMC are outlined

with black boxes in (a)–(d).

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correlation between ST and rainfall over the WMC in

model is opposite to that in observation. Moreover, the

model overestimates the ST–rainfall relationship over the

EMC. As seen from Fig. 3e, the model predicts significant

positive correlations (opposite to those observed) between

ST and rainfall over the WMC after LM0 for January and

after LM3 for February and March, and the ST–rainfall

relationship is also unrealistically predicted after LM1 for

December. The ST–rainfall relationship over the EMC is

overestimated in December and underestimated inMarch

for all leads (Fig. 3f). The model also unrealistically pre-

dicts the ST–rainfall relationship over the EMC in January

and February (Fig. 3f). The relationships between ENSO

andWMC rainfall are unrealistically predicted for all wet-

season months (the correlations between previous ENSO

indices and wet-season WMC rainfall indices are also un-

realistically predicted), while the model predicts the re-

lationship between ENSO and EMC rainfall well,

despite a slight overestimation (figure not shown). Con-

sistently, the model unrealistically predicts the interaction

between ENSO-related winds and the terrain over WMC

after LM1 (figure not shown).

b. Dry season

Rainfall variations over the WMC and the EMC are

both significantly correlated with ENSO in the dry

season (Figs. 4a,c). When the ENSO influence is ex-

cluded, the WMC rainfall is still significantly correlated

with local ST (Fig. 4b). However, signals almost disap-

pear for the EMC rainfall, implying that the rainfall

variation over EMC is mainly modulated by ENSO

(Fig. 4d).

The values shown in Table 1 in the dry columns are the

correlation coefficients for the dry season. Rainfall

variations over the WMC are significantly related to

ENSO and local ST by 3 months in advance. The EMC

rainfall can be significantly forced by ENSO, also by

3 months in advance, but is only significantly related to

local ST by 1 month in advance. These results are con-

sistent with the above analysis.

As an example, we show in Figs. 5a–d the correlations

between rainfall and ST in October for observations and

the CFSv2. For the entire MC, the rainfall–ST re-

lationships are well predicted in various lead months,

although they are slightly overestimated. Unlike in the

wet season, the model realistically predicts the re-

lationship between rainfall and ST overWMC in the dry

season for all leads, and the correlation coefficients in

the model are close to those in observations (Fig. 5e).

Moreover, the ST–rainfall relationship over EMC are

well predicted in the dry season, although they are

overestimated by the model (Fig. 5f). The relationships

FIG. 4. As in Fig. 2, but for the dry season.

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ofWMC and EMC rainfall with ENSO in the dry season

are also well predicted in themodel for all leads (also the

correlations of previous ENSO indices with dry-season

WMC and EMC rainfall indices are realistically pre-

dicted as well), despite some overestimations (figure

not shown).

4. Discussion and conclusions

The roles of remote forcing and local boundary forc-

ing in the variations and prediction of regional MC

rainfall in wet and dry seasons are investigated. Rainfall

averaged over the EMC (118S–108N, 1208–1458E) and

the WMC (138S–78N, 958–1208E) are studied separately

because of the different physical mechanisms for rainfall

variations over the two regions.

In the wet season, the rainfall over both WMC and

EMC are significantly forced by the central-eastern

Pacific SST at least 5 months in advance. The EMC

rainfall variation is also significantly related to local

boundary forcing at least 5 months in advance; however,

the WMC rainfall is insignificantly influenced by local

boundary forcing. The CFSv2 unrealistically predicts

the relationship of WMC rainfall with ENSO and local

boundary forcing (the wind–terrain and air–sea in-

teractions are also unrealistically predicted; figures not

shown) for all wet-season months when lead time is

longer than 1 month, contributing to the poor skill in

predicting the WMC rainfall. On the other hand, the

model predicts the relationship between ENSO and

EMC rainfall well, although the relationship between

the rainfall and local boundary forcing is unrealistically

predicted, leading to higher prediction skill for the

rainfall over EMC compared toWMC in the wet season.

We have also examined these relationships in the en-

semble members of CFSv2 and obtained similar

features.

For the dry season, the WMC rainfall is significantly

related to ENSO and local boundary forcing at least

3 months in advance. However, the EMC rainfall is

mainly modulated by ENSO. The relationships of

ENSO and local boundary forcing with bothWMC and

EMC rainfall are well predicted by the NCEP model,

leading to higher prediction skill of rainfall over the

entire MC in the dry season. Moreover, the linkage

between the skill of rainfall prediction and the re-

lationship of rainfall with local SST implies that over

tropical regions the skill of rainfall prediction is low

FIG. 5. As in Fig. 3, but for October in (a)–(d) and for the dry-season months, July–October (with correlation

coefficients R7–R10), in (e) and (f).

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where the influence of SST is weak, and vice versa (also

see Kumar et al. 2013).

In this study, we have mainly focused on the roles of

external forcing. However, rainfall is also affected by

atmospheric internal processes. Previous studies have

reported that cold surges and MJO contribute to the

variability of convection or rainfall over the MC (e.g.,

Lau et al. 1983; Chang et al. 2005a). In addition, previous

studies have demonstrated deficiencies of climate

models in reproducing the MJO-related variability over

the MC (e.g., Seo et al. 2009; Weaver et al. 2011; Kim

et al. 2014). Can MJO and cold surges influence the in-

terannual variability of rainfall over the MC? The cor-

relation between cold surges and the rainfall in boreal

winter shows that both the EMC rainfall and the WMC

rainfall are significantly correlated to cold surges (figure

not shown), with a coefficient of 0.68 (0.65) for EMC

(WMC). This feature implies that cold surgesmay not be

responsible for the difference in skill between the CFSv2

prediction of EMC and WMC rainfall. The interannual

variability of observed rainfall over both EMC and

WMC is not significantly related to MJO in the wet

season and this feature in CFSv2 hindcast is thus not

analyzed (also because of the short data record in the

model output). The influence of atmospheric internal

processes onMC regional rainfall prediction needs to be

further studied. Furthermore, the problem of poor

model performance for the wet-season WMC rainfall is

not limited to the NCEP model. The complicated re-

lationship (or poor correlation) between WMC rainfall

and ENSO, which may be due to the lack of internal

spatial coherence of rainfall, the rapid change in the sign

of local SST anomalies once the wet season commences,

and the complicated air–sea and wind–terrain in-

teractions over the WMC during the wet season, is dif-

ficult for models to capture (Haylock andMcBride 2001;

Hendon 2003; Chang et al. 2004; Zhu and Shukla 2013;

Zhu and Shukla 2016). Thus, higher resolution and

better representation of air–sea coupling process in

models are important for the prediction of MC rainfall,

especially the WMC rainfall.

Acknowledgments. The authors thank the three

anonymous reviewers for the constructive comments on

the earlier version of the manuscript. This study was

jointly supported by the National Key Scientific Re-

search Plan of China (Grant 2014CB953900), the Nat-

ural Science Foundation of China (Grants 41375081 and

41661144019), the LASW State Key Laboratory Special

Fund (2013LASW-A05), the Jiangsu Collaborative In-

novation Center for Climate Change, and the Zhuhai

Joint Innovative Center for Climate, Environment and

Ecosystem of China.

REFERENCES

Aldrian, E., L. D. Gates, and F. H. Widodo, 2007: Seasonal vari-

ability of Indonesian rainfall in ECHAM4 simulations and in

the reanalyses: The role of ENSO. Theor. Appl. Climatol., 87,

41–59, doi:10.1007/s00704-006-0218-8.

Argueso, D., A. D. Luca, and J. P. Evans, 2016: Precipitation over

urban areas in the western Maritime Continent using a

convection-permitting model. Climate Dyn., 47, 1143–1159,

doi:10.1007/s00382-015-2893-6.

Chang, C.-P., Z. Wang, J. Ju, and T. Li, 2004: On the relationship

between western Maritime Continent monsoon rainfall and

ENSO during northern winter. J. Climate, 17, 665–672,

doi:10.1175/1520-0442(2004)017,0665:OTRBWM.2.0.CO;2.

——, P.A.Harr, andH.-J. Chen, 2005a: Synoptic disturbances over

the equatorial South China Sea and western Maritime Con-

tinent during boreal winter. Mon. Wea. Rev., 133, 489–503,

doi:10.1175/MWR-2868.1.

——, Z. Wang, J. McBride, and C. Liu, 2005b: Annual cycle of

Southeast Asia–Maritime Continent rainfall and the asym-

metric monsoon transition. J. Climate, 18, 287–301, doi:10.1175/

JCLI-3257.1.

Gianotti, R. L., and E. A. B. Eltahir, 2014a: Regional climate

modeling over the Maritime Continent. Part I: New parame-

terization for convective cloud fraction. J. Climate, 27, 1488–

1503, doi:10.1175/JCLI-D-13-00127.1.

——, and ——, 2014b: Regional climate modeling over the Mari-

time Continent. Part II: New parameterization for auto-

conversion of convective rainfall. J. Climate, 27, 1504–1523,

doi:10.1175/JCLI-D-13-00171.1.

Gomyo, M., and K. Koichiro, 2009: Spatial and temporal varia-

tions in rainfall and the ENSO–rainfall relationship over

Sarawak, Malaysian Borneo. SOLA, 5, 41–44, doi:10.2151/

sola.2009-011.

Hamada, J.-I., M. D. Yamanaka, J. Matsumoto, S. Fukao, P. A.

Winarso, and T. Sribimawati, 2002: Spatial and temporal

variations of the rainy season over Indonesia and their link to

ENSO. J. Meteor. Soc. Japan, 80, 285–310, doi:10.2151/

jmsj.80.285.

Hastenrath, S., 1987: Predictability of Java monsoon rainfall

anomalies: A case study. J. ClimateAppl.Meteor., 26, 133–141,

doi:10.1175/1520-0450(1987)026,0133:POJMRA.2.0.CO;2.

Haylock, M., and J. McBride, 2001: Spatial coherence and

predictability of Indonesian wet season rainfall. J. Climate,

14, 3882–3887, doi:10.1175/1520-0442(2001)014,3882:

SCAPOI.2.0.CO;2.

Hendon, H. H., 2003: Indonesian rainfall variability: Impacts of

ENSO and local air–sea interaction. J. Climate, 16, 1775–1790,

doi:10.1175/1520-0442(2003)016,1775:IRVIOE.2.0.CO;2.

Jiang, X., S. Yang, Y. Li, A. Kumar, W. Wang, and Z. Gao, 2013:

Dynamical prediction of the East Asian winter monsoon by

the NCEP Climate Forecast System. J. Geophys. Res. Atmos.,

118, 1312–1328, doi:10.1002/jgrd.50193.——, Y. Li, S. Yang, J. Shu, and G. He, 2015: Interannual variation

of mid-summer heavy rainfall in the eastern edge of the Ti-

betan Plateau. Climate Dyn., 45, 3091–3102, doi:10.1007/

s00382-015-2526-0.

Jo, S., and J.-B. Ahn, 2015: Improvement of CGCM prediction for

wet season precipitation over Maritime Continent using a bias

correction method. Int. J. Climatol., 35, 3721–3732, doi:10.1002/

joc.4232.

Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo,

M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II

8878 JOURNAL OF CL IMATE VOLUME 29

Unauthenticated | Downloaded 11/02/21 02:04 AM UTC

reanalysis (R-2).Bull.Amer.Meteor.Soc.,83, 1631–1643,doi:10.1175/

BAMS-83-11-1631.

Kiladis, G. N., and H. F. Diaz, 1989: Global climatic anomalies

associated with extremes in the Southern Oscillation. J. Cli-

mate, 2, 1069–1090, doi:10.1175/1520-0442(1989)002,1069:

GCAAWE.2.0.CO;2.

Kim, H.-M., P. J. Webster, V. E. Toma, and D. Kim, 2014: Pre-

dictability and prediction skill of the MJO in two operational

forecasting systems. J. Climate, 27, 5364–5378, doi:10.1175/

JCLI-D-13-00480.1.

Kirono, D. G. C., N. J. Tapper, and J. L. McBride, 1999: Doc-

umenting Indonesian rainfall in the 1997/1998 El Niño event.

Phys. Geogr., 20, 422–435.

Kubota,H., R. Shirooka, and J.-I.Hamada, 2011: Interannual rainfall

variability over the eastern Maritime Continent. J. Meteor. Soc.

Japan, 89A, 111–122, doi:10.2151/jmsj.2011-A07.

Kumar, A., M. Chen, and W. Wang, 2013: Understanding pre-

diction skill of seasonal mean precipitation over the tropics.

J. Climate, 26, 5674–5681, doi:10.1175/JCLI-D-12-00731.1.

Lau, K.-M., C.-P. Chang, and P. H. Chan, 1983: Short-term planetary-

scale interactions over the tropics and midlatitudes. Part II:

Winter MONEX period. Mon. Wea. Rev., 111, 1372–1388,

doi:10.1175/1520-0493(1983)111,1372:STPSIO.2.0.CO;2.

Liebmann, B., and C. A. Smith, 1996: Description of a complete

(interpolated) outgoing longwave radiation dataset. Bull.

Amer. Meteor. Soc., 77, 1275–1277.McBride, J. L.,M.R.Haylock, andN.Nicholls, 2003: Relationships

between theMaritimeContinent heat source and the El Niño–Southern Oscillation phenomenon. J. Climate, 16, 2905–2914,

doi:10.1175/1520-0442(2003)016,2905:RBTMCH.2.0.CO;2.

Murakami, T., and A. Sumi, 1982: Southern Hemisphere monsoon

circulation during the 1978–79 WMONEX. Part I: Monthly

mean wind fields. J. Meteor. Soc. Japan, 60, 638–648.

Neale, R., and J. Slingo, 2003: The Maritime Continent and its role

in the global climate: A GCM study. J. Climate, 16, 834–848,

doi:10.1175/1520-0442(2003)016,0834:TMCAIR.2.0.CO;2.

Nicholls, N., 1979: A simple air–sea interaction model. Quart.

J. Roy. Meteor. Soc., 105, 93–105, doi:10.1002/qj.49710544307.

——, 1981: Air–sea interaction and the possibility of long range

weather prediction in the Indonesian Archipelago.Mon. Wea.

Rev., 109, 2435–2443, doi:10.1175/1520-0493(1981)109,2435:

ASIATP.2.0.CO;2.

——, 1984: The Southern Oscillation and Indonesian sea surface

temperature. Mon. Wea. Rev., 112, 424–432, doi:10.1175/

1520-0493(1984)112,0424:TSOAIS.2.0.CO;2.

Qian, J.-H., 2008: Why precipitation is mostly concentrated over

islands in the Maritime Continent. J. Atmos. Sci., 65, 1428–

1441, doi:10.1175/2007JAS2422.1.

Ramage, C. S., 1968: Role of a tropical ‘‘Maritime Continent’’ in

the atmospheric circulation. Mon. Wea. Rev., 96, 365–369,

doi:10.1175/1520-0493(1968)096,0365:ROATMC.2.0.CO;2.

Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey,

and M. G. Schlax, 2007: Daily high-resolution blended ana-

lyses for sea surface temperature. J. Climate, 20, 5473–5496,

doi:10.1175/2007JCLI1824.1.

Rowell, D. P., 2001: Teleconnections between the tropical Pacific

and the Sahel. Quart. J. Roy. Meteor. Soc., 127, 1683–1706,

doi:10.1002/qj.49712757512.

Saha, S., and Coauthors, 2010: TheNCEPClimate Forecast System

Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057,

doi:10.1175/2010BAMS3001.1.

——, and Coauthors, 2014: The NCEP Climate Forecast Sys-

tem version 2. J. Climate, 27, 2185–2208, doi:10.1175/

JCLI-D-12-00823.1.

Schiemann, R., M.-E. Demory, M. S. Mizielinski, M. J. Roberts,

L. C. Shaffrey, J. Strachan, and P. L. Vidale, 2014: The sen-

sitivity of the tropical circulation and Maritime Continent

precipitation to climate model resolution. Climate Dyn., 42,

2455–2468, doi:10.1007/s00382-013-1997-0.

Seo, K.-H., W. Wang, J. Gottschalck, Q. Zhang, J.-K. E. Schemm,

W. R. Higgins, and A. Kummar, 2009: Evaluation of MJO

forecast skill from several statistical and dynamical fore-

cast models. J. Climate, 22, 2372–2388, doi:10.1175/

2008JCLI2421.1.

Slingo, J. M., D. P. Rowell, K. R. Sperber, and F. Nortley, 1999:

On the predictability of the interannual behaviour of the

Madden–Julian oscillation and its relationship with El Niño.Quart. J. Roy. Meteor. Soc., 125, 583–609, doi:10.1002/

qj.49712555411.

Sun, J., H.Wang, andW.Yuan, 2009:A possiblemechanism for the

co-variability of the boreal spring Antarctic Oscillation and

the Yangtze River valley summer rainfall. Int. J. Climatol., 29,

1276–1284, doi:10.1002/joc.1773.

Weaver, S. J., W. Wang, M. Chen, and A. Kumar, 2011: Represen-

tation of MJO variability in the NCEP Climate Forecast Sys-

tem. J. Climate, 24, 4676–4694, doi:10.1175/2011JCLI4188.1.

Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year

monthly analysis based on gauge observations, satellite es-

timates, and numerical model outputs. Bull. Amer. Meteor.

Soc., 78, 2539–2558, doi:10.1175/1520-0477(1997)078,2539:

GPAYMA.2.0.CO;2.

Zhang, T., S. Yang, X. Jiang, and P. Zhao, 2016: Seasonal–

interannual variation and prediction of wet and dry season

rainfall over the Maritime Continent: Roles of ENSO and

monsoon circulation. J. Climate, 29, 3675–3695, doi:10.1175/

JCLI-D-15-0222.1.

Zhu, J., and J. Shukla, 2013: The role of air–sea coupling in seasonal

prediction of Asia–Pacific summer monsoon rainfall.

J. Climate, 26, 5689–5697, doi:10.1175/JCLI-D-13-00190.1.

——, and ——, 2016: Estimation of weather noise in coupled

ocean–atmosphere systems using initialized simulations.

J. Climate, 29, 5675–5688, doi:10.1175/JCLI-D-15-0737.1.

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