ENSO-Monsoon Relationship in a 1000-yr MRI-CGCM Simulation
Akio Kitoh
Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan
Email: kitoh@mri-jma.go.jp
Abstract
The interannual variability of the Indian summer monsoon rainfall and its close relation with ENSO is known(drought conditions over India accompany warm ENSO events and vice versa), but recent observation suggests a
weakening of this ENSO-monsoon relationship and a possible link to global warming is suggested. Here we analyze
ENSO-monsoon relationship in a 1000-year control simulation of the MRI coupled GCM (MRI-CGCM2.2). Anoverall correlation between the JJA Nino3.4 SST and the JJA Indian monsoon rainfall is –0.39 with reasonablecirculation characteristics associated with model ENSO. Simulated ENSO-monsoon relationship reveals long-term
variations from –0.71 to +0.07 in moving 31-year windows. In the first 500 years, the model shows a slight
warming with a temporary weakening of the thermohaline circulation and its recovery. In this period theENSO-monsoon relationship collapses when northern high-latitudes are warm, suggesting a larger temperature
contrast between land and ocean being responsible for a weaker ENSO-monsoon relationship. In the latter 500 yearsof the integration when thermohaline circulation and global mean surface air temperature is stable, ENSO-monsoon
relationship becomes large when tropical interannual variability is large.
Keyword: ENSO, monsoon, decadal variability, global warming, GCM

1.

Introduction

It is well known that the Indian summer(June-September) monsoon rainfall (IMR) becomes
below normal in El Nino years and becomes above
normal in La Nino years. This negative correlation
between the sea surface temperature (SST) anomalies
over the equatorial central/eastern Pacific and the IMR
is called the ENSO-monsoon relationship.
However this relationship, although statisticallysignificant over the available long-term observed
record, shows a decadal fluctuation and even shows acollapse in the relationship in recent years (e.g. Krishna
Kumar et al. 1999). Many hypotheses are raised on thisrecent collapse of the ENSO-monsoon relationship,such as decadal variability (Kripalani and Kulkarni1997), change in seasonality of ENSO cycle(Kawamura et al. 2003), Indian Ocean Dipole mode
(Ashok et al. 2001), Atlantic Oscillation (Chang et al.
2001) and global warming. With respect to the globalwarming, continental warming (Krishna Kumar et al.
1999) or Walker circulation change (Ashrit et al. 2001)
can be candidates for the reason.
For the general circulation model (GCM) results,Ashrit et al. (2001, 2002) analyzed the MPI andCNRM model output and showed a large naturalvariability. On the other hand, the MRI model showeda decrease in the ENSO-monsoon relationship underglobal warming with asymmetric features between El
Nino and La Nina: El Nino–weak IMR relationshipbecomes weak but La Nina–strong IMR relationshipdoes not change (Ashrit et al. 2004).

In this study, we analyze the ENSO-monsoon
relationship and its long-term variability in the
1000-year integration of the MRI coupled GCM. Thismodel (MRI-CGCM2: Yukimoto et al. 2001) has
shown an ability to reproduce a reasonable climate
without flux adjustments (Kitoh 2004).

2.

Model Climate

We used a version of the Meteorological ResearchInstitute global atmosphere-ocean coupled GCM (MRICGCM2, Yukimoto et al. 2001). The model consists ofa T42 L30 AGCM and a global 0.5-2.0° by 2.5° L23
OGCM. The initial condition comes from the previous
flux-adjusted model simulation. This model has been
integrated for 1000 years without flux adjustments.
Figure 1 shows the 1000-year time series of theglobal and annual mean surface air temperature and thestrength of the Atlantic thermohaline circulation. At thebeginning, the flux adjustment was turned off, and thesurface air temperature slightly rises about 0.9°C in thefirst 500 years. The Atlantic thermohaline circulation
initially drops from 19 Sv to 16 Sv, but afterwards it
recovers in several hundred years. The strength in thelast 500 year mean is about 17.7 Sv.
Figure 2 shows the June-August (JJA) mean SSTand precipitation averaged for years 901–1000. The
model reproduces climatological SST and precipitation
distributions reasonably well. Although the modeltends to show a double ITCZ structure in the tropicaleastern Pacific, the Asian monsoon precipitation issimulated very well (Kitoh 2004).

Figure 1. 1000-year time series of (a) annual andglobal mean surface air temperature (°C) and (b)strength of the Atlantic thermohaline circulation(Sv).

Figure 3. 1000-year time series of (a) JJA mean
precipitation over India (IMR) (mm/day) and (b)JJA mean NINO3.4 SST (°C).

Figure 4. Regression fields of the (a) surface airtemperature and (b) precipitation in JJA on thefirst EOF of the near global SST of the 1000-year
integration in JJA.
Figure 2. (a) JJA mean SST and (b) JJA mean
precipitation averaged for year 901-1000.

3.

Model ENSO

The model reproduces a reasonable El Niño andassociated precipitation and atmospheric circulation
changes. Figure 3 shows a 1000-year time-series ofIMR and Nino3.4 SST in JJA. Correlation coefficient
between them is –0.39. Therefore the model shows asignificant negative correlation between IMR andNino3.4 SST. Figure 4 shows the regressions of theJJA surface air temperature and precipitation on theleading EOF mode of the near global SST. There arepositive SST anomalies over the equatorial central andeastern Pacific, the Indian Ocean and the AtlanticOcean. Negative SST anomalies are found over thewestern tropical Pacific. The eastern equatorial IndianOcean is also covered by negative SST anomalies.
Large precipitation anomalies are associated with theSST anomalies in the tropical Pacific and the IndianOcean, implying an eastward displacement of therainfall center. It is noted that negative precipitation
anomalies are noted over India associated with model
El Nino.

4.

Figure 5. 31-year sliding correlations between IMRand NINO3.4 SST anomalies (JJA) during the1000-year simulation.

Evolution of ENSO-Monsoon relationshipIn this section, we describe the temporalvariability of the simulated ENSO-monsoonrelationship. Figure 5 shows the 31-year sliding
correlations between IMR and NINO3.4 SST
anomalies in JJA during the 1000-year simulation. The
correlation coefficient shows a large interdecadal
variability and varies between –0.71 and +0.07. There
are periods with large negative correlations (strong
ENSO-monsoon relationship), such as decades 90s,
350s and 940s, and periods with almost no correlation,such as 470s, 560s and 910s.
In order to capture any simultaneous/precursor
signals of IMR variability, lagged regression analysis isperformed between the time series of Fig. 5 andvarious variables in simultaneous (JJA) as well as thosein one season before (March-May) and two seasons
before (December-February). Figure 6 shows theregression fields of surface air temperature anomaliesin March-May (MAM) on the time series of 31-year
sliding correlations between IMR and NINO3.4 SST
anomalies in JJA. It is noted that there are large
positive temperature anomalies over northern highlatitudes, particularly over the oceans. These features
suggest that warmer Eurasian continent in winter andspring leads to stronger continent-to-ocean temperature
contrast and thus stronger summer monsoon. Thestrong summer monsoon itself would not be affectedmuch by the ENSO-related Walker circulation
anomalies, thus leading to weaker ENSO-monsoon
relationship.

Figure 6. Regression fields of surface air temperaturein March-May (MAM) on the time series of
31-year sliding correlations between IMR andNINO3.4 SST anomalies in JJA (Fig. 5).

Figure 7a and b shows the time series of the31-year sliding mean of IMR and NINO3.4 SST. The
31-year mean IMR varies between 3.29 and 3.94mm/day, while 31-year mean NINO3.4 SST varies
between 27.4 and 28.2°C. Figure 7c and d shows thetime series of interannual standard deviations of IMR
anomalies and NINO3.4 SST anomalies duringrespective 31-year sliding window. They also show
decadal fluctuations with a factor of two.
Correlations between these indices and the timeseries of 31-year sliding correlations between IMR andNINO3.4 SST anomalies in JJA are calculated (Table1) for the entire 1000-year and the first and last 500
year periods. There is a drastic difference between thefirst and the last 500 year periods. In the first half
period, correlation is large in the 31-year sliding mean
IMD and NINO3.4 SST. But correlation with global
mean surface air temperature is also large, and this may
reflect a slight warming (0.9°C/500 years) trend, whichresembles a global warming experiment. With this very
long-term trend, the ENSO-monsoon relationshipbecomes weaker as global temperature as well as
monsoon itself becomes stronger. On the other hand, inthe latter half period, the ENSO-monsoon relationshipis well correlated with the interannual variability ofboth the NINO3.4 SST and IMR, thus suggesting that
decadal-scale variability of the ocean-atmospheresystem can influence the ENSO-monsoon relationship.
It is noted that the ENSO-monsoon relationship canbecome small even with this internal variability such asa period of 910s. Mechanism of this decadal variability
should be investigated further.

References
Ashok et al., GRL, 28, 4499-4502, 2001.
Ashrit et al., GRL, 28, 1727-1730, 2001.
Ashrit et al., JMSJ, 81, 779-803, 2003.
Ashrit et al. (Submitted to JMSJ)
Chang et al., JC, 14, 2376-2380, 2001.
Kawamura et al., JMSJ, 81, 1329-1352, 2003.
Kitoh, JC, 17, 783-802, 2004.
Kripalani and Kulkarni, Int.J.Climatol., 17, 1155-1168, 1997.
Krishna Kumar et al., Science, 284, 2156-2159, 1999.
Rajendran et al., JC, 17, 763-782, 2004.
Yukimoto et al. Pap. Meteor. Geophys., 51, 47-88.

Table 1 Correlation coefficients between several indices and the time series of 31-year sliding correlations
between IMR and NINO3.4 SST anomalies in JJA for the entire 1000 years and first and last 500 year period.
indicesGlobal mean surface air temperature
Atlantic thermohaline circulation
31-year mean IMR
31-year mean NINO3.4 SST
σ (IMR) during 31yr window
σ (NINO3.4 SST) during 31yr window

Year 1-1000
+0.25
+0.15
+0.33
+0.18
–0.20
–0.21

year 1-500
+0.48
+0.29
+0.49
+0.48
–0.08
+0.01year 501-1000
–0.14
+0.04
+0.04
–0.06
–0.42
–0.41

Figure 7. (a) 31-year sliding mean of IMR. (b) 31-year sliding mean of NINO3.4 SST. (c) interannual standard
deviation of IMR anomalies during 31-year sliding window. (d) interannual standard deviation of NINO3.4
SST anomalies during 31-year sliding window.