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Tropical Atmospheric Variability Forced by Oceanic Internal Variability
MARKUS JOCHUM, CLARA DESER, AND ADAM PHILLIPS
National Center for Atmospheric Research, Boulder, Colorado(Manuscript received 10 November 2005, in final form 22 May 2006)
ABSTRACT
Atmospheric general circulation model experiments are conducted to quantify the contribution of internal oceanic variability in the form of tropical instability waves (TIWs) to interannual wind and rainfall
variability in the tropical Pacific. It is found that in the tropical Pacific, along the equator, and near 25°N
and 25°S, TIWs force a significant increase in wind and rainfall variability from interseasonal to interannual
time scales. Because of the stochastic nature of TIWs, this means that climate models that do not take theminto account will underestimate the strength and number of extreme events and may overestimate forecast
capability.

1. Introduction
This study is part of a series of studies aimed at quantifying the effect of tropical instability waves (TIWs) in
the ocean on tropical climate. Caused by instabilities ofthe zonal equatorial ocean currents, TIWs have a period between 20 and 40 days, and on these time scales
they cause variability in SST (Legeckis 1977), cloud
cover (Deser et al. 1993), and wind (Chelton et al. 2001;
Hashizume et al. 2001; Small et al. 2003). Unlike linear
planetary waves, TIWs make a net contribution to theequatorial heat budget because they not only moveheat horizontally toward the equator (Hansen and Paul
1984) but also increase vertical mixing in the upper
ocean and thereby the atmosphere–ocean heat flux (Jochum et al. 2005; Jochum and Murtugudde 2006). Mixing is an irreversible process and therefore TIWs pumpheat from the equatorial mixed layer into the thermocline. Thus, SST in the eastern and central Pacificdepends, among other things, on the level of TIW activity. An important finding for the present study is that
TIWs are nonlinear and therefore vary in strength fromyear to year even under climatological forcing (Jochum
et al. 2004a). This nonlinearity combined with their im-

Corresponding author address: Dr. Markus Jochum, National
Center for Atmospheric Research, P.O. Box 3000, Boulder, CO
80307.
E-mail: markus@ucar.edu
DOI: 10.1175/JCLI4044.1© 2007 American Meteorological Society

JCLI4044

portance to the equatorial heat budget led to the hypothesis that a part of the observed interannual variability in SST can be explained by the stochastic nature
of TIWs. A series of studies then showed that, indeed,in the central Atlantic, eastern Pacific, and western Indian Ocean, approximately one-third of the observed
interannual SST variability is due to instability waves(Jochum et al. 2004b; Jochum and Murtugudde 2004,
2005).
The purpose of the present study is to investigate
whether the stochastic nature of TIWs increases thevariability of the tropical atmosphere on interseasonal–
interannual time scales. The “forced-ocean” studies
cited above could only speculate that internally generated equatorial SST variabilities cause tropical climate
variability by, for example, modifying the seasonal
cycle of the Pacific and Atlantic intertropical convergence zone (ITCZ) or the Indian Ocean monsoon.
Here it will be shown that these internally generated
SST variabilities are indeed large enough to create significant wind and rainfall anomalies over the equatorial
Pacific. To quantify the magnitude by which oceanicinternal variability increases atmospheric variability,
we compare the results from two identical AGCMs,
one forced with climatological SSTs and the otherforced with the same SST plus the interseasonal–
interannual component of the SST variability created
by TIWs. It is important to note that the effect of individual TIWs on SST variability is not addressed here;

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FIG. 1. Climatology of observed tropical Pacific SST (shaded) and rainfall (contour interval: 1 mm day⫺1)simulated from a 25-yr integration of CAM3 forced with observed monthly SST climatology.

FIG. 2. Visualization of the SST anomaly field used in experiment TIW. (a) A sample monthly SST anomaly field
illustrates the spatial scales (contour lines: 0.2°C) and (b) the amplitude and the geographical distribution of thesignal is described by the rms of the SST anomaly field (contour lines: 0.05°C; maximum value: 0.5°C). During theparticular July shown in (a), the equatorial Pacific is warmer than usual over a large part of the eastern basin. For
details see Jochum and Murtugudde (2004).

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FIG. 3. Regression of SST, precipitation, and wind stress anomalies onto the SST anomaly averaged between
3°S–3°N and 145°–139°W. Only the statistically significant regressions are shown [correlation coefficient of at least
0.15 for a 95% confidence interval; the contour interval for precipitation is 0.1 mm day⫺1 (color) and for SST is
0.05°C (black)]. Thus, an SST anomaly of 0.25° (approximately the TIW-induced rms of SST; see Fig. 2) in this area
will at 8°N, 150°W reduce the rainfall rate by 0.3 mm day⫺1 and reduce the easterlies at 1°S, 160°W by 0.004 N m⫺2.
Regressions for SST anomalies farther west and east are smaller because toward the west TIWs are weaker andtoward the east the mean SST is colder (and convection is less sensitive to SST changes).

rather, the present experimental design isolates thenonlinear rectification effect of TIWs and only allows
statements about interseasonal–interannual variability.
The next section describes the experiments, section 3
quantifies the impact of oceanic internal variability on
the atmosphere, and the last section summarizes theresults.

2. The experiments
We analyze the output of two forced AGCM runs. In
both runs the model is identical—version 3 of the Community Atmospheric Model (CAM3) from the National
Center for Atmospheric Research (NCAR) in its T42
configuration. It is a spectral model with 26 layers anda horizontal resolution of approximately 2.8°. It contains state-of-the-art treatments of convection andclouds. Details can be found in Collins et al. (2006).
The two experiments are integrated for 25 yr anddiffer in their SST forcing only. The control experiment(CON) is forced with the monthly climatology of Hurrell et al. (2005, unpublished manuscript; Fig. 1 of thecurrent paper), and the TIW experiment (TIW) is
forced with the sum of the monthly climatology and amonthly SST anomaly field taken from the eddyresolving tropical Pacific Ocean simulation described in
Jochum and Murtugudde (2004). This eddy-resolving
simulation was forced with a climatological seasonal
cycle of atmospheric fluxes, so any deviation from themodel’s climatological seasonal cycle is due to TIWs
(Jochum et al. 2004b). The anomaly field consists of 25
yr (years 21–45 of the 60-yr integration of Jochum and
Murtugudde 2004) of deviations from the monthly climatological values at every grid point. It is restricted to
the Pacific Ocean between 10°S and 10°N. By construction, the long-term mean of the anomaly is zero at every
grid point. It is important to note that the anomalies areaveraged over one month and regridded onto the T42

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FIG. 4. Difference in the rms of the noise in the zonal wind stress between TIW and CON [in percent; only thestatistically significant (95%) differences are shown; contour interval is 10%; the largest value is 35%]. The pattern
is similar for the meridional wind stress (not shown).

grid of the AGCM. Thus, the AGCM does not see theindividual TIWs but only the large-scale nonlinear rectification effect of more or less TIW activity (see introduction). The monthly SST anomaly field is illustrated
in Fig. 2.
Note that the experimental design and hypothesis arevery different from the ones in Small et al. (2003). Small
et al. (2003) analyzed the direct impact that individual
TIWs have on the planetary boundary layer by forcing
a high-resolution AGCM with an SST field that was
highly resolved in time and space. Here the focus is on
the effect that varying TIW activity has on the longerterm variability of the tropical atmosphere. Small et al.
(2003) addresses one possible forcing mechanism foratmospheric intraseasonal variability; the present study
addresses one possible forcing mechanism for atmospheric interseasonal–interannual variability.
The experimental design makes the implict assumption that on eddy time scales, the atmospheric response
does not amplify or weaken the eddies. Idealized studies by Pezzi et al. (2004) and recent high-resolution
coupled GCM studies by H. Seo (2005, personal communication) suggest that this is a reasonable assumption. One could imagine an alternative experimental

setup in which both the atmosphere and the ocean
model fully resolve the eddies. However, the costs forsuch an experiment would be prohibitive and the technical difficulties of damping the eddies without affecting anything else in the model are quite challenging if
not impossible to overcome.

3. Atmospheric response
The autocorrelation length and time scales of the rectified monthly SST anomalies associated with TIWs areapproximately 1000 km (Fig. 2) and 2 months (notshown), respectively. This is large and long enough togenerate a balanced, planetary wave response in thetropical atmosphere (Gill 1980). The analyses of windand rainfall anomalies and rectified TIW-induced SST
anomalies reveal that the response is indeed what is
expected from a linear atmospheric response to localized equatorial heating anomalies (see Gill 1980, hisFig. 1a): the air flows toward the warmer SST, and theresulting southward shift of the ITCZ leads to increased
rainfall on the equator and reduces convergence andrainfall to the north of it (Fig. 3). Note that the atmospheric response depends not only on the level of TIW

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FIG. 5. Ratio of the rms of the annual rainfall maxima between TIW and CON [only statistically significant(95%) values are shown; contour interval is 0.2; the largest equatorial value is 2.8]. For example, in the central
equatorial Pacific, the rms of the rainfall maximum (rainfall in a year’s most rainy month) is about twice as large
in TIW as in CON.

activity but also on the background SST. The maximum
response is found for anomalies between 150° and
140°W; the response is limited by cooler background
SST east of this region and by reduced TIW activity
west of this region. In the rest of this section, we investigate how large this atmospheric response to SST
anomalies is compared to the internal atmospheric variability in CON.
In quantifying the atmospheric response to the stochastic component of TIWs, we limit ourselves to rainfall and wind stress—wind stress because it has direct
implications for the behavior of the coupled system andrainfall because it presents a vertical integral of changes
throughout the atmosphere and is also of societal relevance. The mean and seasonal cycles of both variables
are indistinguishable within the uncertainty (notshown), therefore we will only discuss the deviations
from the climatologies of CON and TIW. In both experiments, the variance of these deviations (hereafter
noise) is red for periods of approximately 100 days andshorter and white for longer periods [not shown; seeFrankignoul and Hasselmann (1977) for a discussion on

stochastic climate models]. However, as shown below,
in some areas the noise is significantly larger in TIWthan in CON. This is a key result of the present study.
As expected, the zonal wind variability increases
along the equator where the SST anomalies are imposed (Fig. 4). There is also a secondary maximum
along 25°N (Fig. 4) that we speculate is caused by atmospheric teleconnections as described in Trenberth etal. (1998). However, the detailed dynamics of the atmospheric teleconnections are beyond the scope of thisstudy; the important result is that TIWs increase thevariability on interseasonal and longer time scales.
The pattern and relative magnitude of the increase in
rainfall variability between TIW and CON is similar tothat for wind (not shown), so instead the variability of
maximum monthly rainfall will be shown here. Because
the rainfall maximum occurs in different months at different locations, the variability was computed for therainiest month at every grid point (e.g., February for
20°N, 150°W, April for 0°, 140°W, June for 20°S, 90°W,etc.). The variability of the rainfall maxima increases
significantly not only along the central equatorial Pa-

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FIG. 6. Rainfall averaged over 3°S–3°N and 150°–100°W for CON (broken line) and TIW (solid line); for better
visibility, only the first 12 yr are shown. While in CON the maximum rainfall stays in a narrow range around 1.2
mm day⫺1, TIW creates extreme maxima (e.g., yr 2) and minima (e.g., yr 11).

cific but also near 20°N, 150°W and 20S°, 90°W (Fig. 5).
The equatorial maximum reaches into the northern
ITCZ and the western Pacific warm pool (recall Fig. 1).
This leads not only to relative extrema but also to large
absolute anomalies. For example, in TIW, the April
rainfall averaged over the eastern Pacific ranges from
0.9 to 1.8 mm yr⫺1, whereas in CON the range is muchless (Fig. 6).

4. Summary and discussion
Intraseasonal eddies in the equatorial oceans are
nonlinear. Therefore their strength varies from year toyear, even under climatological forcing. Since they
make a substantial contribution to the mixed layer heatbudget, they cause interseasonal–interannual variability of SST. Here it was shown that in the equatorial
Pacific, these SST anomalies are large enough to increase the atmospheric variability in rainfall and windstress. Thus, intraseasonal equatorial eddies force interseasonal–interannual equatorial climate variability.
This result comes with the caveat that it is a modelingstudy. It will not be possible to quantify this nonlinear
rectification effect with observations, so the present results are only as trustworthy as the model’s individualcomponents. Both the AGCM and the OGCM used in
the present study are state-of-the-art models, and theresults agree qualitatively with the expectations fromtheory [cf. Fig. 1 of Gill (1980); Fig. 3 of the current
paper]. Therefore the process itself cannot be argued;
only its magnitude can. In the present model, the wind
anomalies have an autocorrelation length of approximately 2000 km and their rms is approximately 10% of
the zonal mean wind stress. Thus, east of the date line,
the noise due to TIWs is similar to that created by the
Madden–Julian oscillation (MJO; Waliser et al. 2003,
2004). The same is also true for the rectified SST
anomalies, as already pointed out by Jochum and Murtugudde (2004). To be clear, the MJO-induced variability in the western Pacific is larger than the TIW-induced
variability anywhere, but the TIWs provide an additional mechanism to create atmospheric variability thatacts in a different region than the MJO.
The present results have one important implication:

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coupled climate models that do not resolve equatorial
ocean eddies will underestimate climate variability on
all time scales, will not give an adequate representationof extreme events, and will overestimate their forecast
capability. This could explain, for example, why current
climate models commonly lack tropical intraseasonalvariability (Deser et al. 2006; Lin et al. 2006). A recent
study by Roberts et al. (2004) suggests that resolving
oceanic eddies in coupled GCMs does indeed improvethe variability of SST. It should be pointed out thatafter minor adjustments to the horizontal viscosity
scheme, the NCAR ocean model realistically represents TIWs. This suggests that other GCMs, many ofwhich also use a resolution in the equatorial ocean ofapproximately 1° ⫻ 1/3°, should be able to generate
TIWs as well. Therefore, the increased and improved
atmospheric variability in GCMs would come at no additional computational costs.
Acknowledgments. This research was supported by
NSF through NCAR and by NOAA funds for mesoscale air–sea interaction. The authors are grateful to
Raghu Murtugudde for many discussions and to Bill
Large for his enthusiasm.
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