1 MAY 2017

HE ET AL.

3197

Atmospheric and Oceanic Origins of Tropical Precipitation Variability
JIE HE
Princeton University, and NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

CLARA DESER
National Center for Atmospheric Research,a Boulder, Colorado

BRIAN J. SODEN
Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida(Manuscript received 1 October 2016, in final form 14 January 2017)
ABSTRACTThe intrinsic atmospheric and ocean-induced tropical precipitation variability is studied using millennial
control simulations with various degrees of ocean coupling. A comparison between the coupled simulationand the atmosphere-only simulation with climatological sea surface temperatures (SSTs) shows that a substantial amount of tropical precipitation variability is generated without oceanic influence. This intrinsic
atmospheric variability features a red noise spectrum from daily to monthly time scales and a white noise
spectrum beyond the monthly time scale. The oceanic impact is inappreciable for submonthly time scales but
important at interannual and longer time scales. For time scales longer than a year, it enhances precipitation
variability throughout much of the tropical oceans and suppresses it in some subtropical areas, preferentiallyin the summer hemisphere. The sign of the ocean-induced precipitation variability can be inferred from thelocal precipitation–SST relationship, which largely reflects the local feedbacks between the two, althoughnonlocal forcing associated with El Niño–Southern Oscillation also plays a role. The thermodynamic anddynamic nature of the ocean-induced precipitation variability is studied by comparing the fully coupled andslab ocean simulations. For time scales longer than a year, equatorial precipitation variability is almost entirely driven by ocean circulation, except in the Atlantic Ocean. In the rest of the tropics, ocean-induced
precipitation variability is dominated by mixed layer thermodynamics. Additional analyses indicate that bothdynamic and thermodynamic oceanic processes are important for establishing the leading modes of largescale tropical precipitation variability. On the other hand, ocean dynamics likely dampens tropical Pacificvariability at multidecadal time scales and beyond.

1. Introduction
The great dependence of human society and natural
ecosystems on rainfall makes precipitation variability anessential aspect of Earth’s climate. Precipitation variability
Denotes content that is immediately available upon publication as open access.

Supplemental information related to this paper is available at the
Journals Online website: http://dx.doi.org/10.1175/JCLI-D-16-0714.s1.
a
The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author e-mail: Jie He, jie.he@noaa.gov

is particularly important in the tropics, as it not only affects local water supply but also regulates climate remotely through the generation and dispersion of Rossby
waves (e.g., Sardeshmukh and Hoskins 1988; Barsugli
and Battisti 1998; Schneider et al. 2003). Tropical precipitation variability ranges over many time scales, fromshort-lived weather systems, such as thunderstorms, tointraseasonal phenomena, such as the Madden–Julian
oscillation (MJO), to even longer interannual anddecadal variations, including El Niño–Southern Oscillation (ENSO). Understanding the origin of tropicalprecipitation variability across the entire temporal range isamong the fundamental objectives of climate science.
The natural variability of precipitation can be considered, to first order, as a superposition of an internal

DOI: 10.1175/JCLI-D-16-0714.1
Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

3198

JOURNAL OF CLIMATE

part due to intrinsic atmospheric dynamics and an external part via the interaction of atmosphere with landand ocean. In the tropics, it is well accepted that theocean plays a crucial role through variations in sea
surface temperatures (SSTs). Above-normal SSTs often increase the boundary layer moist static energy andinduce anomalous convection. A well-known example
is the El Niño event, during which the Pacific warm
pool shifts eastward, as do deep convection and heavy
rainfall. Owing to the ocean’s dynamic control on El
Niño–related SST variations, this ocean-induced precipitation variability exhibits predictability beyond the
2–3-week time scale of weather noise.
The ocean’s role in generating tropical precipitation
variability is reflected in the positive local relationshipbetween precipitation and SST over much of thetropical oceans (Trenberth and Shea 2005). However,certain areas (e.g., the southwestern Indian Ocean andthe western Pacific Ocean in summer) feature a negative precipitation–SST relationship (Trenberth andShea 2005; Wu et al. 2006). In these regions, a large
portion of precipitation variability likely results fromremote SST forcing or intrinsic atmospheric dynamics(Wang et al. 2005; Wu et al. 2006).
As a nonlinear system, the atmosphere exhibits substantial variability of its own (Lorenz 1963). The chaotic
behavior of the atmosphere is often simplified as white
noise, with no predictability beyond a few weeks(Hasselmann 1976; Frankignoul and Hasselmann 1977).
The importance of the atmospheric intrinsic variability
has been long recognized in the midlatitudes, where theatmosphere is commonly treated as a stochastic forcing
term in the coupled atmosphere–ocean system (e.g.,Frankignoul 1985; Barsugli and Battisti 1998). At low
frequencies, the atmospheric variability is amplified by
feedbacks from the slowly evolving ocean state. The
coupling between the atmosphere and ocean in the midlatitudes is primarily thermodynamic (e.g., Frankignoul
and Hasselmann 1977; Barsugli and Battisti 1998; Deseret al. 2003); however, dynamical coupling may also be
important in some cases, for example for some aspects of
the Pacific decadal oscillation (e.g., Schneider and Miller
2001; Schneider and Cornuelle 2005; Kwon and Deser
2007; Newman et al. 2016) and the Atlantic multidecadaloscillation (e.g., Zhang 2008; Zhang et al. 2016; Delworth
et al. 2017), although studies by Clement et al. (2015,2016) have challenged the latter.
In the tropics, however, precipitation variability due
to intrinsic atmospheric dynamics has been far lessstudied compared to its ocean-induced counterpart.
The historical emphasis on the ocean-induced precipitation variability is largely due to its greater predictability, but the extent to which it represents theVOLUME 30

total precipitation variability at different time scales
has yet to be determined. In addition, there is a lack ofconsensus on whether ocean dynamics plays a leadingrole in tropical air–sea interaction. For example, somestudies found ocean circulation to be important indriving decadal-scale climate variability in the tropical
Pacific (e.g., Jin 2001; Luo and Yamagata 2001), whereas
other studies argued for thermodynamic coupling between the atmosphere and the ocean mixed layer (e.g.,
Clement et al. 2011; Okumura 2013; Zhang et al. 2014).
The Community Earth System Model (CESM) Large
Ensemble Project (Kay et al. 2015), which includes
millennial control simulations with fully coupled, slab
ocean and atmosphere-only configurations, provides anideal opportunity for addressing these issues. In thisstudy, we will analyze the contribution of the atmosphere and ocean to tropical precipitation variability
over a wide range of time scales from daily to multidecadal. The influence of intrinsic atmospheric processes is assessed from the atmosphere-only simulation
forced by a prescribed climatological seasonal cycle of
SSTs taken from the fully coupled control simulation.
By comparing the atmosphere-only simulation with the
fully coupled one, we obtain the ‘‘ocean induced’’ component of precipitation variability. We note that thisocean-induced component can arise from intrinsic ocean
variability and ocean–atmosphere coupling; we will
mainly discuss their combined effect. The thermodynamic
and dynamic nature of the ocean-induced precipitation
variability is studied by means of the slab ocean and fulldepth ocean coupled simulations.
The rest of the study is organized as follows. Section 2
describes the data and methods. The results are presented in section 3, where we quantify the atmosphericand oceanic contributions to tropical precipitation variability at different time scales. The oceanic contribution
is then explained by understanding the nature of local
precipitation–SST feedbacks. Finally, we present a brief
analysis of the spatial characteristics of the leading modesof atmospheric and ocean-induced precipitation variability. The results are summarized and discussed in
section 4, along with implications for the simulation ofhistorical tropical precipitation trends.

2. Data and methods
a. Model simulations
We analyze three long preindustrial control simulations from the CESM (Hurrell et al. 2013): an 1800-yr
fully coupled run, a 900-yr slab ocean run, and a 2100-yr
atmosphere-only run. These simulations differ in theirdegree of ocean coupling but are all fully coupled with

1 MAY 2017

HE ET AL.

land. All three simulations use the Community Atmosphere Model (CAM), version 5, and the Community
Land Model, version 4, and are run at approximately
18 resolution for all model components. These simulations were conducted as part of the CESM Large Ensemble Project (Kay et al. 2015).
In the slab ocean run (SlabOcean), CAM5 is coupled
to a slab ocean model, the depth of which is taken as theclimatological annual mean mixed layer depth from thefully coupled run (Coupled). A fixed ‘‘q flux’’ term,prescribed as a seasonal-varying climatological sea surface heat flux from the Coupled run, is added to the SST
equation following Bitz et al. (2012). This accounts forthe effect of ocean dynamics on the SST mean state. The
SlabOcean run allows for thermodynamic coupling between the atmosphere and the ocean mixed layer but
eliminates any impact from variations in the ocean circulation. We will estimate the impact of dynamic ocean
coupling from the difference between the Coupled andthe SlabOcean runs.
Because of the lack of dynamical ocean coupling, thetropical SST climatology in the SlabOcean run is slightly
warmer than that in the Coupled run in all seasons(Fig. S1 in the supplementary material). As a result, theSlabOcean run has slightly larger precipitation in its
climatology than the Coupled run in certain oceanic
regions (discussed later), which may affect the comparison of precipitation variability and remains a caveat in
the interpretation of our results.
The atmosphere-only run uses the monthly climatological seasonal cycle of SST and sea ice from the Coupled run as its lower boundary condition. We will referto this simulation as ClimSST. Because of the absence of
SST variability beyond the mean seasonal cycle, precipitation variability over ocean primarily results fromatmospheric intrinsic dynamics. Strictly speaking,however, it also contains land–atmosphere coupled variability, particularly in monsoon regions, where caution
must be taken for interpreting the ClimSST run. Nevertheless, since all the simulations have the same landmodel, presumably intrinsic land–atmosphere variability
will be the same in all of our simulations and will notaffect their comparison. The difference between the
Coupled run and the ClimSST run captures the oceaninduced component of precipitation variability. Note that
this precipitation variability results from internal ocean
processes, as well as ocean–atmosphere coupling; ourexperiments do not allow us to differentiate between
the two.
To estimate the impact of the lack of two-way coupling in the atmosphere-only simulations, we conduct a
100-yr uncoupled simulation forced by time-varying
monthly mean SSTs taken from years 401–500 of the

3199

Coupled run. This simulation will be referred to as
FullSST.

b. Observational datasets
To benchmark the simulated precipitation variability,
we analyze pentad (5-day mean) precipitation during
1979–2014 from the Global Precipitation Climatology
Project (GPCP; Adler et al. 2003; Huffman et al. 2009).
The data are recorded on a 2.58 3 2.58 grid and can be
downloaded from the JISAO website (http://research.
jisao.washington.edu/data_sets/gpcp/daily/pentad.html).
Note that fitting the rain gauge–based observations to arectangular spatial grid inevitably introduces some spatial and temporal averaging, which affects variability.
Therefore, we only present the observed results for the
purpose of comparing against the simulations, which arealso computed on a rectangular grid.
We also analyze monthly precipitation during 1979–
2014 from the CPC Merged Analysis of Precipitation(CMAP; http://www.esrl.noaa.gov/psd/data/gridded/data.
cmap.html) and monthly surface temperature during
1980–2014 from the Goddard Institute for Space Studies(GISS) surface temperature analysis (http://www.esrl.
noaa.gov/psd/data/gridded/data.gistemp.html). The spatial resolution is 2.58 3 2.58 for the CMAP precipitationand 28 3 28 for the GISS surface temperature. The GISS
surface temperature combines data from the Global
Historical Climatology Network, version 3 (meteorological stations), the Extended Reconstructed Sea Surface
Temperature, version 4 (ocean areas), and the ScientificCommittee on Antarctic Research data (Antarctic stations), as detailed in Hansen et al. (2010).

c. Methods
We first remove the linear trend from all datasets, inorder to minimize the impact of climate drift from thesimulations and anthropogenic forcing from the observations. The climate drift in the simulations is minimal
because the simulations are started from a spun-up oceanstate (Kay et al. 2015). Before comparing the simulations
with the observations, we interpolate the observed data
onto the model grid. When calculating the observed air–sea relationships using the CMAP, which contains missing data, the calculation is done only if at least 70% of themonths in a given grid box have data.
We divide the total precipitation variability into several
frequency bands of interest, including daily–annual,interannual–decadal, decadal, and multidecadal. Eachfrequency band is isolated by applying Lanczos filteringin the time domain (Duchon 1979). To estimate the amplitude of precipitation variance for each frequency band,
we calculate the standard deviation of the filtered data.
We validate this approach by summing up the power

3200

JOURNAL OF CLIMATEVOLUME 30

FIG. 1. Mean (contours) and standard deviation (color shading) of pentad precipitation
anomalies from (a) the Coupled run, (b) the GPCP, and (c) their difference. Results from the
Coupled run are calculated as the average of 50 nonoverlapping 36-yr segments (1800 yr intotal), whereas results for the GPCP are calculated using the available 36 yr (1979–2014).
Contour interval is 3 mm day21, starting at 3 mm day21, in (a) and (b) and 1 mm day21, startingat 1 mm day21, in (c). Dashed contours in (c) indicate negative values. Zero contours areomitted. Areas where the difference in variance is not significant at the 99% level based on the
F test are stippled in (c). Here and in the rest of the paper, the equivalent sample sizes forstatistical tests are determined following (Zwiers and von Storch 1995).

spectra over the specific frequency band, which yields
consistent results (figures not shown).

3. Resultsa. Overview of precipitation variance across all timescales
It is useful to first examine the total precipitation varianceover all time scales resolved by the data. For the pentad
GPCP data, we calculate the total variance as the standard
deviation of pentad precipitation anomalies during the
36 years (1979–2014) that are available (Fig. 1b). To estimate the total variance from the fully coupled CESM forthe same frequency span as the observations, we calculate
the standard deviation of pentad precipitation output fromeach of the 50 nonoverlapping 36-yr segments of the Coupled run and then average across the 50 segments (Fig. 1a).
Both the simulated and observed total precipitation
variance fields closely follow the pattern of theircorresponding precipitation climatologies (contours in
Figs. 1a,b). For example, regions of large variancecorrespond to areas with high climatological precipitation,such as the ITCZ, whereas low variance can be found inregions of low mean rainfall, such as the southeastern
Pacific. The similarity between patterns of variance andclimatology is expected, since precipitation is a positive
definite quantity and fluctuates about its mean within therange of zero and its maximum amplitude. There are large
biases in the simulated precipitation variance (Fig. 1c).
The CESM overestimates the precipitation variance overmost of the ITCZ and underestimates it over the equatorial Pacific and the off-equatorial Atlantic Ocean. The
pattern of biases in variance is very similar to the patternof biases in climatology (contour in Fig. 1c).
In Fig. 2, we compare the power spectra of precipitation from GPCP (black lines) and the fully coupled
CESM simulation (blue lines) in six tropical regions,which are indicated by the purple boxes in Fig. 2g. In most

1 MAY 2017

HE ET AL.

3201

FIG. 2. (a)–(f) Precipitation power spectra averaged over selected regions in the tropics from the GPCP (black) and the Coupled (blue),
the SlabOcean (green), and the ClimSST (red) runs. The dashed vertical lines denote the 30-day and 1-yr time scales. (g) The location of
the corresponding regions is shown as the purple boxes. The observed spectra are calculated as the average of two partially overlapping
24-yr segments from the 36-yr pentad data. The simulated spectra are calculated as the average of partially overlapping 100-yr segmentsfrom the daily output; each segment overlaps the next one by 50 yr. Annual mean precipitation climatology from the Coupled simulation(shading) and the GPCP (contours) in (g). Contour interval is 3 mm day21, starting at 3 mm day21.

3202

JOURNAL OF CLIMATEof these regions (except the equatorial Pacific), the fully
coupled CESM overestimates precipitation variance attime scales shorter than 50 days. The general overestimation of high-frequency precipitation variance isconsistent with previous findings that most climate models
rain too frequently compared to observations, particularly
at light rain rates (Pendergrass and Hartmann 2014).
For the three regions in the tropical Pacific (Figs. 2a,d,e),
the fully coupled CESM reproduces the observed spectra reasonably well. However, the imprint of an overly
vigorous ENSO in the model is evident in most regions,with a spectral peak in the frequency band of approximately 2–8 yr21 that is narrower and stronger comparedto the observations (Fig. 2a). An earlier version of
CESM with CAM, version 4, also overestimated the
ENSO amplitude (Deser et al. 2012a), which is likely
associated with an excessive negative cloud feedback(Tang et al. 2016). For the other three regions outside
the Pacific Ocean (Figs. 2b,c,f), the CESM overestimates
precipitation variance at almost all frequencies.
Figure 2 also shows the precipitation power spectrafrom the SlabOcean (green lines) and the ClimSST (redlines) runs. (Note that the ClimSST run is forced withmonthly climatological SST and thereby does not include submonthly SST variability; however, a brief
analysis in Fig. S2 of the supplementary material indicates that such an experimental setup should not affectthe interpretation of our results.) The spectra from the
ClimSST run resemble that arising from a red noise
process with increasing variance from daily to monthly
time scales. Beyond the monthly time scale, the uncoupled spectra are virtually flat, which is consistent
with the common assumption that atmospheric intrinsic
variability is essentially white noise (e.g., Hasselmann
1976; Frankignoul and Hasselmann 1977). The precipitation variability from the SlabOcean run also
features a red noise spectrum. The time scale beyond
which the spectra become white varies with region, but it
is generally close to or longer than a year. Compared to
the ClimSST run, the precipitation variability from the
SlabOcean run has similar variance at high frequencies
and generally larger variance at low frequencies. This isanalogous to the first-order explanation for the generation of climate variability in midlatitudes, where atmospheric intrinsic variability is generally amplified at low
frequencies through the influence of more slowly evolving SST anomalies, which in turn are due to the integrated
thermodynamic response of the ocean mixed layer tointrinsic atmospheric variability (Hasselmann 1976;
Frankignoul and Hasselmann 1977; Frankignoul 1985).
Considering the substantial oceanic impact on precipitation variability beyond the monthly time scale andthe close relationship between precipitation variability

VOLUME 30

and precipitation climatology, it is necessary to examine
whether oceanic variability also affects precipitation
climatology. As shown in Fig. 3a, there are considerable
discrepancies in the long-term mean precipitation between the Coupled and ClimSST runs, despite theirvirtually identical SST climatologies (Fig. S1). The inclusion of SST variability increases climatological precipitation in the equatorial Pacific by 25%–45% andmoderately reduces it by 15% or less in certain offequatorial regions, including the northeastern andsoutheastern Pacific, the Maritime Continent and theadjacent oceanic regions, the North Atlantic, and thesouthwestern Indian Ocean. The increase at the equatorial Pacific is most likely due to the nonlinear rectification effect of SST forcing (i.e., positive SST anomalies
can increase precipitation substantially, whereas negative SST anomalies only have a minor effect since themean precipitation is low to begin with). There are also
noticeable discrepancies over land, where SST variability generally increases precipitation climatology
north of the equator and reduces it south of the equator.
As we shall see, the sign of the oceanic contribution toprecipitation climatology is generally consistent withthat to precipitation variance.
The climatological precipitation discrepancies in the
ClimSST run can be attributed to either the lack of SST
variability or the lack of two-way coupling; the latter
eliminates any SST response to atmospheric forcing andcould degrade the simulation of tropical climate (e.g.,
Wang et al. 2005; Wu et al. 2006). In Fig. 3b, we compare
the Coupled run with the FullSST run, in which themonthly varying SSTs from a 100-yr segment of the
Coupled run are prescribed. The FullSST run does notsubstantially underestimate precipitation climatology
from the Coupled run in the equatorial Pacific but stillshows generally similar discrepancies as those in the
ClimSST run over the rest of the tropics, indicating thatthe lack of two-way coupling could cause the differences
between ClimSST and Coupled runs outside of theequatorial Pacific. This is in conflict with previous studies,
which suggested that two-way coupling is not necessary insimulating the time-averaged climate (Skinner et al. 2012;
He and Soden 2016). Note, however, that the FullSST run
is only 100 years long and therefore might not provide arobust estimation of precipitation climatology (note thatthe differences between Coupled and FullSST runs are
generally less than 15% over the tropical ocean and notstatistically significant).
Discrepancies in precipitation climatology can also befound between the Coupled and SlabOcean runs (Fig. 3c).
This may be partially due to the SST biases (Fig. S1).
However, in the equatorial Pacific, precipitation climatology is 15%–35% larger in the Coupled run despite the1 MAY 2017

HE ET AL.

3203

FIG. 3. Fractional difference in annual mean precipitation climatology (color shading) between (a) Coupled and ClimSST runs, (b) Coupled and FullSST runs, and (c) Coupled andSlabOcean runs. The fractional difference is shown as a percentage relative to the Coupledclimatology. Annual mean precipitation climatology from the Coupled run is plotted as contours. Contour interval is 3 mm day21, starting at 3 mm day21. Areas where the difference invariance is not significant at the 99% level based on the Student’s t test are stippled. There ismuch more stippling in (b) than in (a) and (c), mainly because of the relatively small sample sizeof the FullSST run.

lower SST, which suggests that coupling with ocean dynamics increases precipitation climatology. In addition,
the Coupled run shows larger precipitation climatology
than the SlabOcean run in the north equatorial Atlantic(by 5%–45%) and parts of northern Africa (by 5%–25%).

b. Precipitation variance at daily to yearly time scales
The regional average precipitation spectra in Fig. 2
suggest that the relative role of ocean in driving precipitation variability varies with time scale. In Fig. 4, we
present precipitation variance from the Coupled and
ClimSST runs at subannual and beyond-annual timescales. Since the uncoupled precipitation variance isdistributed according to an approximately red noise
process in the frequency domain (Fig. 2), its spatial
pattern at individual frequency bands is also similar tothe pattern of precipitation climatology (Figs. 4b,f).
For the subannual time scale, the ocean-induced precipitation variability (i.e., Coupled – ClimSST runs) issmall (Fig. 4c) and consists of less than 30% of the total
variance in almost the entire tropics, except at the central
equatorial Pacific (Fig. 4d). Particularly at the submonthly time scale, precipitation variance is very similar

between the Coupled and ClimSST runs (Fig. 2). The
insignificance of ocean at submonthly time scales islargely due to the lack of substantial SST variability onthese time scales (Fig. S3 in the supplementary material).
However, this result might be susceptible to the model’s
inadequate simulation of phenomena, such as the MJO,
which might be better represented with improved parameterization and higher resolution (Boyle et al. 2015).
As shown in the right column of Fig. 4, the relative
impact of ocean is larger at lower frequencies. For timescales longer than a year, ocean amplifies precipitation
variability over most of the tropical oceans. Particularly
within 108 of the equator, oceanic variability generally
contributes to more than 30% of the total precipitation
variance (Fig. 4h). In the equatorial Pacific, precipitation variability is almost entirely driven by ocean(values .80%). In contrast to the ClimSST run(Fig. 4f), precipitation variance in the Coupled run(Fig. 4e) has its maximum values on the equatorwardsides of the climatology maxima (ITCZ and SPCZ) inthe Pacific at this frequency band. In certain regions(e.g., the southwestern Indian Ocean, the Bay of Bengal,
and the northeastern Pacific), however, ocean suppresses

3204

JOURNAL OF CLIMATEVOLUME 30

FIG. 4. Precipitation standard deviation (mm day21; color shading) from the (a),(e) Coupled run; (b),(f) ClimSST
run; and (c),(g) their difference and (d),(h) their fractional difference, using (left) 365-day high-pass and (right)
365-day low-pass daily precipitation anomalies. Contours show the annual mean precipitation climatology from the
Coupled run in (a),(e),(c),(g),(d),(h) and from the ClimSST run in (b) and (f). Contour interval is 3 mm day21,
staring at 3 mm day21. Note that different scales are used to plot the results for the two time scales. The scales of thecolor bars are chosen in a way that the uncoupled precipitation variance [(b) and (f)] appears similarly for the twotime scales. Areas where the difference in variance is not significant at the 99% level based on the F test are stippledin (c),(g),(d),(h). The lack of stippling in (c) and (d) is mainly due to the large sample size.

precipitation variability (Figs. 4g,h). The oceanic contribution to low-frequency precipitation variability as well as
its seasonality will be further discussed in the next two
sections.

c. Precipitation variance at interannual and longer
time scales
In this section, we present tropical precipitation variability on interannual and longer time scales (.2 yr).
Subdividing this frequency band into three frequency
bands (2–10, 10–50, and .50 yr) does not provide additional insight, as their characteristics are very similar to
those based on .2 yr for the analyses presented here
(Figs. S4 and S5 in the supplementary material). In thisand the following sections, we analyze each season separately. Results will be presented for December–February(DJF) and June–August (JJA); results for March–May
and September–November are similar to those for DJF
and JJA, respectively, and will not be presented here.
The patterns of precipitation variance on interannual
and longer time scales in the ClimSST run are again
similar to their climatologies, both in DJF and JJA (notshown). Here, we focus on the role of ocean by comparing the fractional difference in precipitation varianceamong the three model configurations. Ocean enhances
precipitation variability over most tropical ocean regions, particularly at the equatorial Pacific, where it
generates over 90% of the total variability in DJF andover 70% in JJA (Figs. 5a and 6a, respectively). On theother hand, ocean suppresses precipitation variability incertain off-equatorial oceanic regions. For DJF(Fig. 5a), these regions include the southwestern Indian
Ocean, the center of the South Pacific convergence zone
(SPCZ) and the subtropical central North Pacific (thelatter only for the decadal time scale and beyond;Fig. S4). For JJA (Fig. 6a), these regions include thenortheastern Pacific, the South China Sea, and the Bayof Bengal. Interestingly, the negative oceanic contribution primarily occurs in the summer hemisphere. We willfurther analyze the pattern of the ocean-induced precipitation variance in the next section. The magnitude of
the positive oceanic contribution peaks at the interannual time scale in the equatorial Pacific and theequatorial Indian Ocean, consistent with Fig. 2. The
impact of the ocean also extends to land regions. In
particular, it induces positive precipitation variance inNorth and central Africa in DJF (Fig. 5a) and in SouthAmerica in JJA (Fig. 6a).

1 MAY 2017

HE ET AL.

3205

FIG. 5. Fractional difference in precipitation standard deviation (color shading) between(a) Coupled and ClimSST, (b) SlabOcean and ClimSST, and (c) Coupled and SlabOcean using
yearly DJF precipitation anomalies. The fractional difference is shown as a percentage relative
to the Coupled standard deviation. DJF mean precipitation climatology from the Coupled run
is plotted as contours. Contour interval is 3 mm day21, starting at 3 mm day21. Areas where thedifference in variance is not significant at the 99% level based on the F test are stippled.

In the off-equatorial regions, both the positive andnegative ocean-induced precipitation variability arelargely reproduced by the SlabOcean run and are
therefore primarily associated with mixed layer thermodynamic processes (Figs. 5b and 6b). At the equator,
however, most of the precipitation variability requires
coupling with ocean dynamics (Figs. 5c and 6c). The DJF
equatorial Atlantic is an exception where mixed layer
thermodynamics dominate. In addition, the Coupled
run generally simulates less precipitation variability inthe off-equatorial regions than the SlabOcean run,which likely suggests a negative contribution from ocean
circulation but could also be caused by the lower climatological SST in the Coupled run (Fig. S1).

d. Local air–sea relationships
The pattern of the ocean-induced precipitation variability shown in Figs. 7a and 8a suggests positive andnegative feedbacks between precipitation and SST
anomalies. If we assume that these feedbacks are primarily local and instantaneous for the time scales considered here (i.e., precipitation and SST adjust to eachother’s variations well within a year), we should be able tounderstand them through the simultaneous, pointwise
correlation between precipitation and SST. Of course,
local correlations may also reflect nonlocal controls on
precipitation and SST (e.g., those due to atmospheric andoceanic teleconnections). These will also be taken into
account in our discussion below.
Figures 7b and 8b show the local pointwise regression
coefficients between precipitation and surface temperature anomalies in DJF and JJA, respectively, from the
Coupled run (mm day21 K21). Note that no time filtering has been applied, other than the use of seasonal means.
In both DJF and JJA, the precipitation–SST regression ispositive over most of the tropical oceans, with values exceeding 2.5 mm day21 K21 over the western equatorial
Pacific and the equatorial Indian Ocean. On the other
hand, the regression is negative in the center of the ITCZ
and SPCZ, as well as some areas of the subtropical oceans(e.g., the southwestern Indian Ocean in DJF and the Bayof Bengal and the South China Sea in JJA) and many land
regions, particularly in the summer hemisphere.
Previous studies (Trenberth and Shea 2005; Wang et al.
2005; Wu et al. 2006) showed that the local precipitation–
SST relationship characterizes the basic nature of3206

JOURNAL OF CLIMATEVOLUME 30

FIG. 6. As in Fig. 5, but for JJA.

precipitation–SST interaction. In the absence of remote
SST forcing (such as that associated with ENSO, which
will be discussed later), the relationship is positive when
SST forces precipitation and negative when precipitation
forces SST. A negative relationship in the absence ofnonlocal effects is primarily associated with cloud radiative cooling and cold air downdrafts associated withconvection (e.g., Waliser and Graham 1993; Sud et al.
1999). In the absence of precipitation-forcing SST, precipitation generally increases with increasing local SST,and vice versa. We can determine this from theatmosphere-only simulations with prescribed SST
anomalies, in which a positive precipitation–SST relationship is found throughout the tropical oceans(Wang et al. 2005; Wu et al. 2006). In a coupled system,
however, the relationship can be negative. In thiscase, a positive precipitation anomaly can cause a local
cooling of SST, which will act to reduce precipitationand suppress the initial precipitation anomaly.
Therefore, we can expect ocean to locally amplify
precipitation variability where SST forcing dominates(i.e., positive precipitation–SST regression) and locallysuppress precipitation variability where precipitation
forcing dominates (i.e., negative precipitation–SST regression). Indeed, the pattern of the precipitation–SST
regression value over ocean (Figs. 7b and 8b) is very
similar to that of the oceanic contribution to precipitation

variance (i.e., Coupled vs ClimSST; Figs. 8a and 9a). In
general, ocean amplifies precipitation variability in regions of positive precipitation–SST regression, and viceversa. However, there are exceptions in certain partsof the ITCZ (i.e., the northern ITCZ in DJF and boththe northern and southern ITCZ in JJA), where ocean
amplifies precipitation variability despite a negative
precipitation–SST regression. A common characteristicof these regions is the presence of large horizontal SST
gradients. It is likely that most of the local precipitation
variability is driven by variations in the SST gradientrather than the amplitude of the SST itself (Lindzen andNigam 1987; Back and Bretherton 2009; Li and Carbone2012). Indeed, in the northern ITCZ region in DJF, wefind a moderate negative correlation between precipitation and the local Laplacian of SST (Fig. S6 in the
supplementary material). On the other hand, suchnegative precipitation–SST regressions can also result
from remote SST forcing and, in that case, may notserve as an indicator of negative ocean-induced precipitation variability, as we shall show next.
Previous studies suggested that ENSO-related SST
anomalies may be particularly effective in modulating
the precipitation–SST relationship through its ability
to shift precipitation at large scales (Trenberth andShea 2005; Wu et al. 2006). In Figs. 9 and 10, we calculate the precipitation–SST regression for ENSO years

1 MAY 2017

HE ET AL.

3207

FIG. 7. (a) Difference in precipitation standard deviation between the Coupled andSlabOcean runs using yearly DJF mean precipitation, and linear regressions of precipitation
anomaly onto surface temperature anomaly using yearly DJF output (b) from the Coupled run
and (c) from CMAP and GISS surface temperature analysis. Regions where more than 30% of
the data are missing are filled in gray. Contours are the DJF precipitation climatology from the
Coupled run in (a) and (b) and the observed DJF precipitation climatology in (c). Contour
interval is 3 mm day21, starting from 3 mm day21. Stippling in (a) indicates that the differencein variance is not significant at the 99% level based on the F test, whereas stippling in (b) and(c) indicates that the linear correlation between precipitation and surface temperature is not
significant at the 99% level based on the two-sided Student’s t test.

(Figs. 9a and 10a) and non-ENSO years (Figs. 9b and10b) separately. The difference between the two (Figs. 9cand 10c) provides an estimation of the impact of ENSO.
The ENSO-induced precipitation–SST regression(Figs. 9c and 10c) has about the same magnitude as thetotal precipitation–SST regression (Figs. 7b and 8b). The
pattern of the ENSO-induced precipitation–SST regression (Figs. 9c and 10c) is essentially determined bywhether the local SST and precipitation anomalies havethe same sign during ENSO (Fig. S8 in the supplementarymaterial). In the equatorial Pacific and the eastern Indian
Ocean, where precipitation increases during El Niño anddecreases during La Niña, ENSO drives a positive
precipitation–SST regression. The positive regression
suggests that the SSTs could be more likely to cross theconvection threshold during ENSO, causing an enhanced
precipitation response to the SST anomaly. In the offequatorial Pacific and the western Indian Ocean, ENSO

drives a negative precipitation–SST regression. These
regions experience a lack of rainfall because of the drastic
equatorial shift of convection during El Niño, despite thepositive local SST anomaly. The clear-sky condition may
in turn enhance the warming of the SST (Klein et al. 1999;
Shinoda et al. 2004; Chiang and Lintner 2005). Interestingly, the remote influence of equatorial Pacific SST
anomalies is only effective at the interannual time scaleand virtually nonexistent at the decadal time scale andbeyond (latter not shown).
Figures 9d and 10d show the difference in precipitation variance between ENSO years and non-ENSO
years. The ENSO-induced precipitation variability is
positive over most of the tropics and is most appreciable
over regions where ENSO substantially affects theprecipitation–SST regression (Figs. 9c and 10c). Note
that, even in regions where ENSO induces a negative
precipitation–SST regression, precipitation variability is

3208

JOURNAL OF CLIMATEVOLUME 30

FIG. 8. As in Fig. 7, but for JJA.

still generally enhanced by ENSO. Such enhanced
variability is most likely a result of remote SST forcing,since the ENSO-induced local precipitation–SST feedbacks act to suppress precipitation variability.
To benchmark the CESM simulated precipitation–
SST regression, we present the observed precipitation–
SST regression in Figs. 7c and 8c. Because of the relatively
short record of the observations, most of the tropics do
not show statistically significant correlations (stippling in
Figs. 7c and 8c). Nevertheless, the model is able to reproduce the basic structure of the observed precipitation–
SST regression, and most of the discrepancies between
the two are related to biases in the model’s mean state.
For example, the tendency for negative regression values
within the centers of the precipitation maxima (SPCZ,
ITCZ, and the Maritime Continent) is present to somedegree in observations, although the exact geographicallocations may not coincide with those simulated as a result of the biased mean state in the model. On the other
hand, some of the discrepancies appear unrelated to theclimatological biases and are likely a result of the misrepresentation of the precipitation–SST feedbacks. For
example, in DJF, the CESM underestimates the negative
regression in the Maritime Continent and the adjacentoceanic regions and the positive regression in the westernequatorial Pacific; it also overestimates the positive
regression in the equatorial Atlantic. In JJA, the negative regression in the summer Asian monsoon regions
is generally stronger and extends farther south in theobservations.

e. Spatial modes of precipitation variability
The substantial precipitation variability simulated in the
SlabOcean and ClimSST runs merits more detailed analyses on its spatial and temporal characteristics. To that
end, we performed and compared EOF analyses in various tropical regions at interannual (,10 yr), decadal (10–50 yr), and multidecadal (.50 yr) time scales. We only
present results based on EOFs over the tropical Pacific, aregion that has historically attracted great attention.
Figures 11 and 12 show global maps of annual mean
precipitation and sea level pressure (SLP) anomalies
correlated with the first principal component (PC) of
precipitation anomalies within the tropical Pacific region (outlined by the red rectangle) for each time scale.
The Coupled (Figs. 11a–c), SlabOcean (Figs. 11d–f), and
ClimSST (Fig. 12) runs all simulate spatially coherent
precipitation and circulation patterns within the tropical
Pacific at all time scales. Unlike the Coupled run, whichshows a decrease in explained variance with increasing1 MAY 2017

HE ET AL.

3209

FIG. 9. Linear regression of precipitation anomaly onto surface temperature anomaly (color
shading) using yearly DJF output from the Coupled run for (a) ENSO years, during which theyearly DJF SST anomaly in the Niño-3.4 region exceeds one standard deviation, (b) non-ENSO
years, during which the SST anomaly of Niño-3.4 is less than 30% standard deviation, and(c) their difference. (d) Difference in the standard deviation of yearly DJF precipitation between the ENSO years and non-ENSO years in the Coupled run. DJF precipitation climatology
from the Coupled run is plotted as contours. Contour interval is 3 mm day21. Stippling in(a),(b),(d) indicates the results of the statistical tests and is plotted the same as in Fig. 7.

time scale, the SlabOcean run shows an increase, and the
ClimSST run shows almost no change. Indeed, the variance explained in the Coupled run diminishes by nearly afactor of 2 (from 38.4% to 23.1%), whereas that in the
SlabOcean run more than doubles (from 13.9% to 29.3%)
between interannual and multidecadal time scales. The
variance explained in the ClimSST run is much smaller
than that in the other two runs at all time scales (6%–7%).
For all three time scales, the first EOF of the Coupled
run exhibits an ENSO-like precipitation pattern in thetropical Pacific with teleconnections extending to nearly
the entire globe. Particularly at the interannual timescale, the spatial coherence of the global precipitationand SLP patterns associated with the first EOF in the

Coupled run (Fig. 11a) is unmatched by that fromthe SlabOcean (Fig. 11d) and ClimSST (Fig. 12a) runs. The
first EOF of the SlabOcean run features a similar ENSOlike pattern at the multidecadal time scale, although theremote teleconnections differ (Fig. 11f). However, at theinterannual (Fig. 11d) and decadal (Fig. 11e) time scale,the leading mode of variability shows a distinctive structure with an out-of-phase relationship between theequatorial Pacific and the northern subtropics. For these
two time scales, the ENSO-like mode appears as thesecond EOF in the SlabOcean run (Fig. S9 in the supplementary material). Although the ENSO-like mode in
the SlabOcean run is generally similar to that in the
Coupled run, it shows an embedded negative precipitation

3210

JOURNAL OF CLIMATEVOLUME 30

FIG. 10. As in Fig. 9, but for JJA.

anomaly right at the equator, which is absent in the
Coupled run. This difference can be found for all threetime scales and likely reflects the strong dependence of
precipitation variability on coupling with ocean dynamics
at the equatorial Pacific (Figs. 5c and 6c).
The first mode of the ClimSST run features a zonally
banded pattern in the tropical Pacific and explains a
relatively small portion of the total precipitation variance (Fig. 12). This pattern differs substantially from the
ENSO-like pattern because of the lack of ocean coupling. Compared to the Coupled and SlabOcean runs,
the ClimSST run has the most temporally consistent
EOF modes, with similar patterns and amplitude acrossall time scales. This is in agreement with the previous
conclusion that the uncoupled tropical precipitation
variability is essentially a white noise beyond the monthly
time scale.
An interesting feature of Fig. 11 is the pronounced
extratropical circulation patterns associated with thetropical Pacific precipitation variability, which occur in

the Coupled run at all time scales and in the SlabOcean
run mainly at the multidecadal time scale. For example,
most of these show a negative SLP anomaly in the North
Pacific and a positive SLP anomaly in the southeastern
Pacific, as well as connections to the Southern Ocean
and Atlantic basin of varying strength. It is well known
that tropical precipitation anomalies can modulate extratropical circulation via the generation and dispersionof Rossby waves (e.g., Sardeshmukh and Hoskins 1988;
Barsugli and Battisti 1998; Schneider et al. 2003). On theother hand, the extratropical circulation can also energize tropical climate variability. It has been shown thatthe atmospheric stochastic forcing from both the North
Pacific (e.g., Pierce et al. 2000; Di Lorenzo et al. 2015)
and the South Pacific (e.g., Okumura 2013; Zhang et al.
2014) are particularly important for the development of
the ENSO-like pattern of variability. A question that
often appears in these studies is how ocean coupling
facilitates these tropical–extratropical teleconnections.
Here, we present a brief analysis on this question.

1 MAY 2017

HE ET AL.

3211

FIG. 11. Linear correlation of precipitation (shading) and SLP (contour) anomalies with the leading PC of
precipitation anomalies in the Indo-Pacific region, which is marked by the red boxes. Data are the annual mean
output filtered by (a),(d) 10-yr high pass, (b),(e) 10–50-yr bandpass, and (c),(f) 50-yr low pass. Results are from the(left) Coupled and (right) SlabOcean runs. Contour interval is 0.2. Dashed contours represent negative values. The
zero contour is thickened. Variance explained by EOF1 is shown in the title of each panel.

In Fig. 13, we show global maps of annual mean precipitation and SLP correlations with the leading mode of
SLP variability in the extratropical South Pacific (158–
708S, 1708E–708W; outlined by the red box) at each timescale. The region chosen for the EOF analysis is definedin the same way as by Okumura (2013), who investigatedthe decadal tropical–extratropical teleconnections in asimilar way using the Community Climate System
Model, version 4. Both the Coupled and SlabOcean runs
show a local Pacific–South American pattern (PSA;
with a low pressure anomaly off the coast of Chile and an
adjacent high pressure anomaly to its south) embedded
within a larger southern annular mode structure at all

three time scales, consistent with Okumura (2013). For
all time scales in the Coupled run and the SlabOceanrun, the PSA is associated with a meridional dipole
pattern of precipitation in the South Pacific; however,
the tropical precipitation anomalies are not consistent.
The ClimSST run also simulates a PSA mode, indicatingthat such a mode can be generated through atmosphericintrinsic dynamics. However, its correlation with tropical precipitation is negligible (figures not shown).
A key distinction between the two coupled model
configurations is that the connection to the tropical Pacific (both precipitation and SLP) strengthens as thetime scale lengthens in the case of the SlabOcean run

3212

JOURNAL OF CLIMATEVOLUME 30

tropical Pacific at the multidecadal time scale comparedto the interannual time scale, while the Coupled run
shows the opposite (Fig. S10 in the supplementary material). EOF analyses of tropical Pacific SST further
show that multidecadal ENSO-like variability is stronger in the SlabOcean run than the Coupled run (Fig. S11
in the supplementary material). This indicates a possible
negative role of ocean dynamics in the tropical–
extratropical teleconnections and the development of
ENSO-like variability at the multidecadal time scale.

4. Conclusions and discussion

FIG. 12. As in Fig. 11, but for the ClimSST run.

and weakens in the case of the Coupled run. Moreover,in the Coupled run on the multidecadal time scale, thetropical Pacific linkage to the PSA is mainly confined tosouth of the equator (Fig. 13c), which differs from the
ENSO-like pattern based directly on the tropical precipitation EOF (Fig. 11c). No such distinction is evident
for the SlabOcean run at the multidecadal time scale,
which exhibits nearly identical global-scale patterns
whether based on the first EOF (EOF1) of South Pacific
SLP (Fig. 13f) or tropical Pacific precipitation (Fig. 11f).
If the analysis is based on the EOF of North Pacific SLP
instead of the South Pacific, similar results are found:that is, the SlabOcean run also exhibits stronger teleconnections between the northern extratropics and theWe have studied tropical precipitation variability infully coupled, slab ocean, and fixed-SST runs whose
component models, when active, are identical. The main
goal was to identify atmospheric and oceanic origins oftropical precipitation variability and to understand thethermodynamic and dynamic nature of the oceaninduced precipitation variability. We obtain the atmospheric component of precipitation variability from thefixed-SST run and the ocean-induced component fromthe difference between the fully coupled and fixed-SST
runs. Note that this definition of ocean-induced precipitation variability does not differentiate between theimpact of internal oceanic variability and that of ocean–
atmosphere interaction. While internal oceanic variability has been shown to produce a multidecadal spectral
peak in SST in the northern North Atlantic in association
with fluctuations of the thermohaline circulation(Delworth and Zeng 2016; Delworth et al. 2017), we
find no evidence for spectral peaks beyond that associated with the interannual ENSO phenomenon in either tropical precipitation (Fig. 2) or tropical SST(Fig. S3) in the fully coupled simulation. Thus, most of
the ocean-induced tropical precipitation variability
studied here likely originates from ocean–atmosphereinteraction instead of internal oceanic variability.
Further experiments are needed to provide conclusive
results.
Our analyses of the fixed-SST simulation show that
substantial precipitation variability is internally generated by the atmosphere. This atmospheric intrinsic
precipitation variability exhibits a red spectrum at submonthly time scales and a white spectrum at longer timescales. The pattern of the atmospheric intrinsic precipitation variance is similar to the pattern of precipitation climatology at all time scales examined(submonthly–multidecadal). Because of the lack of
substantial SST variability at submonthly time scales,
the high-frequency precipitation variability is almost
identical with and without oceanic influence. At lower
frequencies, however, the ocean has a strong impact.

1 MAY 2017

HE ET AL.

3213

FIG. 13. Linear correlation of precipitation (shading) and SLP (contour) anomalies with the leading PC of SLP
anomalies in the South Pacific region, which is marked by the red boxes. Data are the annual mean output filteredby (a),(d) 10-yr high pass, (b),(e) 10–50-yr bandpass, and (c),(f) 50-yr low pass. Results are from the (left) Coupledand (right) SlabOcean runs. Contour interval is 0.2. Dashed contours represent negative values. The zero contour isthickened. Variance explained by EOF1 is shown in the title of each panel.

At the interannual time scale and beyond, the role ofocean in generating precipitation variability varies with
region. Ocean increases precipitation variability overmost of the tropical oceans but reduces it over someparts of the subtropics preferentially in the summer
hemisphere. In the equatorial Pacific and equatorial
Indian Ocean, the ocean-induced precipitation is primarily driven by ocean dynamics. Over the rest of thetropical oceans, however, it is dominated by the thermodynamic coupling with the ocean mixed layer.
In addition to its impact on precipitation variability,
ocean also affects the simulation of precipitation climatology. In particular, precipitation climatology along
the equator of the Pacific is reduced by nearly half when

SST variability is not included. This has great implications for the future design of AMIP-type experiments,
since it has been a common practice to neglect SST
variability in simulations that only aim to study the timemean changes in precipitation (e.g., externally forced
mean climate change). Our results showed that the inclusion of accurate SST variability is necessary for simulating the time mean precipitation in this key region.
The pattern of the ocean-induced precipitation variability is similar to the pattern of the pointwise precipitation–
SST regression. In regions where the regression is positive,
SST forcing dominates the precipitation–SST relationship and amplifies precipitation variability. In regions where the regression is negative, however, the SST

3214

JOURNAL OF CLIMATE

anomaly is largely a response to the negative precipitation
feedback and subsequently acts to suppress precipitation
variability. ENSO is also important for the establishment of
the precipitation–SST relationship by enhancing the SST
forcing at the equator and inducing a negative precipitation
feedback in the off-equatorial regions. Interestingly, thenegative precipitation–SST relationship generally occurs at
the center of the ITCZ and in the summer hemisphere
subtropics, where the sea surface is relatively warm. Observational studies showed that precipitation and cloudformation tend to keep SST below a certain maximum
value (e.g., Waliser and Graham 1993; Sud et al. 1999), andnegative precipitation feedback is therefore likely stronger
in regions of warm SST. However, future studies are needed
to assess the simulation of such processes in climate models.
Assessing the simulation of the ocean-induced precipitation variability is a challenge partly because of the
difficulty in separating the ocean-induced variance fromthe total variance in observations. However, the patternsimilarity between the ocean-induced precipitation variance and the precipitation–SST regression allows us toassess the former through the latter. The coupled CESM
simulates overall reasonable precipitation–SST regression;
its biases are largely associated with biases in the positionof the ITCZ.
Although we have focused mostly on the relationshipbetween precipitation and SST anomalies, fluctuationsin the SST gradient can also be important in generating
precipitation variability. In particular, the observational
study by Li and Carbone (2012) showed a greater role
for the local SST gradient than the local SST in triggering individual rainfall events over the tropical western Pacific. On a seasonal mean basis, however, we foundthat precipitation variability is more closely related to the
local SST rather than the local SST gradient in both observations and the fully coupled simulation (Figs. S6 andS7), except in the center of the northern ITCZ in DJF anddirectly along the equator in the western Pacific in both DJF
and JJA. The issue of the relative importance of the local
SST versus SST gradient on precipitation variability as afunction of temporal scale merits additional investigation.
We briefly presented an EOF analysis to study thespatial characteristics of the precipitation variability
simulated by the three CESM configurations. The leading
modes of tropical Pacific precipitation show very different
amplitude and spatial structures with and without ocean
coupling. A similar ENSO-like precipitation pattern appears in the Coupled run at decadal and multidecadal timescales and in the SlabOcean run at all time scales longer
than one year. In the Coupled run, the ENSO-like mode
and its teleconnection with the extatropical circulation weaken as the time scale lengthens. In contrast, theSlabOcean run shows a stronger ENSO-like mode and a

VOLUME 30

more robust tropical–extratropical teleconnection at longer time scales. This suggests that the role of ocean dynamics in driving tropical Pacific variability likely
diminishes with increasing time scale. Particularly attime scales longer than 50 years, the ENSO-like variability in the SlabOcean run becomes stronger thanthat in the Coupled run.
The stronger multidecadal variability in the SlabOcean
run indicates a negative feedback between ocean circulation and SST variability at low frequencies, which was
proposed by Di Nezio (2011). At the interannual timescale, ENSO is initialized by changes in the thermocline depth associated with the reflected Kelvin wave. The
Kelvin wave is generated because wind-driven Rossby
waves do not arrive at the western boundary in phase (e.g.,
Cane and Sarachik 1981). At longer time scales, however,
the wind forcing and ocean adjustment are able to maintaina closer balance, resulting in a weaker Kelvin wave andweaker thermocline forcing. As a result, changes in thethermocline no longer drive the SST variability but are instead in opposite phase to SST, providing a negative feedback. Such a mechanism likely causes the Coupled run tohave a weaker ENSO-like variability than the SlabOcean
run at the multidecadal time scale.
Natural precipitation variability at multidecadal andlonger time scales has historically received less attention
than its higher-frequency counterpart, mainly becauseof the lack of long-term observations. However, quantifying and understanding such variability is crucial forthe interpretation of climate change, both in the recent
past and coming decades. Many studies show that climate changes during a 50-yr span can be dominated bynatural variability at regional scales, especially in theextratropics in winter (Deser et al. 2012b, 2014; Kayet al. 2015; Deser et al. 2016). This makes the assessmentof historical climate simulations difficult, since thechronologies of the simulated internal variability neednot match each other or the observation. Figures 14a
and 14b show that the intermodel spread of 50-yr trends(1955–2005) in tropical precipitation from historicalsimulations in the CMIP5 archive is substantially larger
than the multimodel mean trend, which is typically interpreted as the radiatively forced component. The
small amplitude of the multimodel mean trend is in partdue to the cancellation among the trends from the individual models. However, the spread in trends acrossthe 35 members of the CESM large ensemble (Fig. 14d)
is also larger than that model’s forced response (given by
the ensemble mean: Fig. 14c). This suggests that internalvariability is large enough to mask the forced response,at least in the CESM, with implications for interpretingthe observed record. Furthermore, the spread in 50-yr
trends due to internal variability (Fig. 14d) is only

1 MAY 2017

HE ET AL.

3215

FIG. 14. The 50-yr DJF precipitation trend from the CMIP5 historical run and the CESM
historical and preindustrial control runs. The historical trend is taken from the period of December 1955–November 2005. (a) The ensemble mean and (b) intermodel standard deviationof the historical precipitation trend from 40 CMIP5 models. (c) The ensemble mean and(d) intermember standard deviation of precipitation trend from the 35-member CESM historical runs, which only differ from each other slightly in their initial conditions. (e) The
standard deviation of precipitation trends using nonoverlapping 50-yr segments from the
ClimSST run. DJF precipitation climatology from the respective simulations is shown in contours, with an interval of 3 mm day21 and starting at 3 mm day21. [The 40 CMIP5 models are
BCC_CSM1.1, BCC_CSM1.1(m), BNU-ESM, CanESM2, CCSM4, CESM1(BGC), CESM1(CAM5), CESM1(FASTCHEM), CESM1(WACCM), CMCC-CESM, CMCC-CM, CMCCCMS, CNRM-CM5, CNRM-CM5.2, EC-EARTH, FGOALS-g2, GFDL CM2.1, GFDL CM3,
GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H, GISS-E2-H-CC, GISS-E2-R, GISS-E2-R-CC,
HadCM3, HadGEM2-CC, HadGEM2-ES, INM-CM4.0, IPSL-CM5A-LR, IPSL-CM5A-MR,
IPSL-CM5B-LR, MIROC4h, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESMLR, MPI-ESM-MR, MPI-ESM-P, MRI-CGCM3, and MRI-ESM1. Expansions of acronyms
are available online at http://www.ametsoc.org/PubsAcronymList.]modestly less than the spread across the CMIP5 models(Fig. 14b), suggesting that internal climate variability,
rather than the models’ structural differences, is themain cause of spread.
Deser et al. (2012b) showed that the low-frequency
precipitation variability in the extratropics is dominatedby atmospheric intrinsic dynamics. In the tropics, on theother hand, ocean is expected to have a much greater
impact on precipitation. However, the spread in trends
from nonoverlapping 50-yr segments of the ClimSST
run is comparable to that from the 35-member CESM
large ensemble (and that from the Coupled control run;

3216

JOURNAL OF CLIMATEnot shown), except at the equator (Fig. 14e). We haveshown that the leading modes of large-scale tropicalprecipitation variability require interaction with theocean. Therefore, the evolution of SSTs holds the key tofuture predictions of large-scale tropical precipitation.
On regional scales, however, atmospheric intrinsic dynamics account for a substantial proportion of tropicalprecipitation variability, particularly over land regions,which may indicate limited predictability beyond thesynoptic time scale.
Since all of our simulations were conducted with the
CESM, future studies are encouraged to test the robustness of our results, particularly with higher-resolution
models, which may more faithfully capture ENSO variability and its teleconnections (Jia et al. 2015) as well as
submonthly variability.
Acknowledgments. We wish to thank Drs. Amy
Clement, Ben Kirtman, and Pedro DiNezio for helpful discussions during the course of this work andDrs. Nathaniel Johnson and Liwei Jia for their internal
review at GFDL. We appreciate the thoughtful comments
and suggestions of the three anonymous reviewers.
Thanks also go to Dr. Isla Simpson for providing the
FullSST simulation and Dr. Todd Mitchell for calculating
the pentad GPCP data. Jie He is supported by the Visiting
Scientist Program at Princeton University.
REFERENCES
Adler, R. F., and Coauthors, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation
analysis (1979–present). J. Hydrometeor., 4, 1147–1167,
doi:10.1175/1525-7541(2003)004,1147:TVGPCP.2.0.CO;2.
Back, L. E., and C. S. Bretherton, 2009: On the relationship between SST gradients, boundary layer winds, and convergenceover the tropical oceans. J. Climate, 22, 4182–4196,
doi:10.1175/2009JCLI2392.1.
Barsugli, J. J., and D. S. Battisti, 1998: The basic effects of atmosphere–
ocean thermal coupling on midlatitude variability. J. Atmos.
Sci., 55, 477–493, doi:10.1175/1520-0469(1998)055,0477:
TBEOAO.2.0.CO;2.
Bitz, C. M., K. M. Shell, P. R. Gent, D. A. Bailey, G. Danabasoglu,
K. C. Armour, M. M. Holland, and J. T. Kiehl, 2012: Climate
sensitivity of the Community Climate System Model, version 4.
J. Climate, 25, 3053–3070, doi:10.1175/JCLI-D-11-00290.1.
Boyle, J. S., S. A. Klein, D. D. Lucas, H.-Y. Ma, J. Tannahill, and S. Xie,
2015: The parametric sensitivity of CAM5’s MJO. J. Geophys.
Res. Atmos., 120, 1424–1444, doi:10.1002/2014JD022507.
Cane, M. A., and E. Sarachik, 1981: The response of a linear baroclinic
equatorial ocean to periodic forcing. J. Mar. Res., 39, 651–693.
Chiang, J. C. H., and B. R. Lintner, 2005: Mechanisms of remotetropical surface warming during El Niño. J. Climate, 18, 4130–
4149, doi:10.1175/JCLI3529.1.
Clement, A., P. DiNezio, and C. Deser, 2011: Rethinking theocean’s role in the Southern Oscillation. J. Climate, 24, 4056–
4072, doi:10.1175/2011JCLI3973.1.

VOLUME 30——, K. Bellomo, L. N. Murphy, M. A. Cane, T. Mauritsen, G. Rädel,and B. Stevens, 2015: The Atlantic Multidecadal Oscillation
without a role for ocean circulation. Science, 350, 320–324,
doi:10.1126/science.aab3980.
——, M. A. Cane, L. N. Murphy, K. Bellomo, T. Mauritsen, and
B. Stevens, 2016: Response to comment on ‘‘The Atlantic Multidecadal Oscillation without a role for ocean circulation.’’ Science,
352, 1527, doi:10.1126/science.aaf2575.
Delworth, T. L., and F. Zeng, 2016: The impact of the North Atlantic Oscillation on climate through its influence on the
Atlantic meridional overturning circulation. J. Climate, 29,
941–962, doi:10.1175/JCLI-D-15-0396.1.
——, ——, L. Zhang, R. Zhang, G. A. Vecchi, and X. Yang, 2017:
The central role of ocean dynamics in connecting the North
Atlantic Oscillation to the Atlantic multidecadal oscillation.
J. Climate, doi:10.1175/JCLI-D-16-0358.1, in press.
Deser, C., M. A. Alexander, and M. S. Timlin, 2003: Understanding thepersistence of sea surface temperature anomalies in midlatitudes.
J. Climate, 16, 57–72, doi:10.1175/1520-0442(2003)016,0057:
UTPOSS.2.0.CO;2.
——, and Coauthors, 2012a: ENSO and Pacific decadal variability
in the Community Climate System Model version 4.
J. Climate, 25, 2622–2651, doi:10.1175/JCLI-D-11-00301.1.
——, A. Phillips, V. Bourdette, and H. Teng, 2012b: Uncertainty inclimate change projections: The role of internal variability.
Climate Dyn., 38, 527–546, doi:10.1007/s00382-010-0977-x.
——, ——, M. A. Alexander, and B. V. Smoliak, 2014: Projecting
North American climate over the next 50 years: Uncertainty
due to internal variability. J. Climate, 27, 2271–2296,
doi:10.1175/JCLI-D-13-00451.1.
——, L. Terray, and A. S. Phillips, 2016: Forced and internal components of winter air temperature trends over North America
during the past 50 years: Mechanisms and implications.
J. Climate, 29, 2237–2258, doi:10.1175/JCLI-D-15-0304.1.
Di Lorenzo, E., G. Liguori, N. Schneider, J. C. Furtado, B. T.
Anderson, and M. A. Alexander, 2015: ENSO and meridionalmodes: A null hypothesis for Pacific climate variability. Geophys. Res. Lett., 42, 9440–9448, doi:10.1002/2015GL066281.
Di Nezio, P., 2011: Mechanisms of tropical Pacific climate change:
Beyond the Bjerknes feedback. Ph.D. dissertation, Dept. of
Meteorology and Physical Oceanography, University of Miami, 244 pp. [Available online at http://scholarlyrepository.
miami.edu/oa_dissertations/567.]
Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 1016–1022, doi:10.1175/
1520-0450(1979)018,1016:LFIOAT.2.0.CO;2.
Frankignoul, C., 1985: Sea surface temperature anomalies, planetary waves, and air–sea feedback in the middle latitudes. Rev.
Geophys., 23, 357–390, doi:10.1029/RG023i004p00357.
——, and K. Hasselmann, 1977: Stochastic climate models. Part II:
Application to sea-surface temperature anomalies and thermocline variability. Tellus, 29A, 289–305, doi:10.1111/
j.2153-3490.1977.tb00740.x.
Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface
temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/
2010RG000345.
Hasselmann, K., 1976: Stochastic climate models. Part I: Theory.
Tellus, 28A, 473–485, doi:10.1111/j.2153-3490.1976.tb00696.x.
He, J., and B. J. Soden, 2016: Does the lack of coupling in SSTforced atmosphere-only models limit their usefulness for climate change studies? J. Climate, 29, 4317–4325, doi:10.1175/
JCLI-D-14-00597.1.

1 MAY 2017

HE ET AL.

Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP Version 2.1.
Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.
Hurrell, J. W., and Coauthors, 2013: The Community Earth System
Model: A framework for collaborative research. Bull. Amer.
Meteor. Soc., 94, 1339–1360, doi:10.1175/BAMS-D-12-00121.1.
Jia, L., and Coauthors, 2015: Improved seasonal prediction oftemperature and precipitation over land in a high-resolution
GFDL climate model. J. Climate, 28, 2044–2062, doi:10.1175/
JCLI-D-14-00112.1.
Jin, F.-F., 2001: Low-frequency modes of tropical ocean dynamics.
J. Climate, 14, 3874–3881, doi:10.1175/1520-0442(2001)014,3874:
LFMOTO.2.0.CO;2.
Kay, J. E., and Coauthors, 2015: The Community Earth System
Model (CESM) Large Ensemble Project: A community resource for studying climate change in the presence of internal
climate variability. Bull. Amer. Meteor. Soc., 96, 1333–1349,
doi:10.1175/BAMS-D-13-00255.1.
Klein, S. A., B. J. Soden, and N.-C. Lau, 1999: Remote sea surface
temperature variations during ENSO: Evidence for a tropicalatmospheric bridge. J. Climate, 12, 917–932, doi:10.1175/
1520-0442(1999)012,0917:RSSTVD.2.0.CO;2.
Kwon, Y.-O., and C. Deser, 2007: North Pacific decadal variability
in the Community Climate System Model version 2.
J. Climate, 20, 2416–2433, doi:10.1175/JCLI4103.1.
Li, Y., and R. E. Carbone, 2012: Excitation of rainfall over thetropical western Pacific. J. Atmos. Sci., 69, 2983–2994,
doi:10.1175/JAS-D-11-0245.1.
Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface
temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 2418–2436, doi:10.1175/
1520-0469(1987)044,2418:OTROSS.2.0.CO;2.
Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20,
130–141, doi:10.1175/1520-0469(1963)020,0130:DNF.2.0.CO;2.
Luo, J.-J., and T. Yamagata, 2001: Long-term El Niño–Southern
Oscillation (ENSO)-like variation with special emphasis on
the South Pacific. J. Geophys. Res., 106, 22 211–22 227,
doi:10.1029/2000JC000471.
Newman, M., and Coauthors, 2016: The Pacific decadal oscillation, revisited. J. Climate, 29, 4399–4427, doi:10.1175/
JCLI-D-15-0508.1.
Okumura, Y. M., 2013: Origins of tropical Pacific decadal variability:
Role of stochastic atmospheric forcing from the South Pacific.
J. Climate, 26, 9791–9796, doi:10.1175/JCLI-D-13-00448.1.
Pendergrass, A. G., and D. L. Hartmann, 2014: Two modes ofchange of the distribution of rain. J. Climate, 27, 8357–8371,
doi:10.1175/JCLI-D-14-00182.1.
Pierce, D. W., T. P. Barnett, and M. Latif, 2000: Connections between
the Pacific Ocean tropics and midlatitudes on decadal timescales.
J. Climate, 13, 1173–1194, doi:10.1175/1520-0442(2000)013,1173:
CBTPOT.2.0.CO;2.
Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of global
rotational flow by steady idealized tropical divergence. J. Atmos.

3217

Sci., 45, 1228–1251, doi:10.1175/1520-0469(1988)045,1228:
TGOGRF.2.0.CO;2.
Schneider, E. K., L. Bengtsson, and Z.-Z. Hu, 2003: Forcing of Northern Hemisphere climate trends. J. Atmos.
Sci., 60, 1504–1521, doi:10.1175/1520-0469(2003)060,1504:
FONHCT.2.0.CO;2.
Schneider, N., and A. J. Miller, 2001: Predicting western North
Pacific Ocean climate. J. Climate, 14, 3997–4002, doi:10.1175/
1520-0442(2001)014,3997:PWNPOC.2.0.CO;2.
——, and B. D. Cornuelle, 2005: The forcing of the Pacific decadaloscillation. J. Climate, 18, 4355–4373, doi:10.1175/JCLI3527.1.
Shinoda, T., M. A. Alexander, and H. H. Hendon, 2004: Remoteresponse of the Indian Ocean to interannual SST variations in
the tropical Pacific. J. Climate, 17, 362–372, doi:10.1175/
1520-0442(2004)017,0362:RROTIO.2.0.CO;2.
Skinner, C. B., M. Ashfaq, and N. S. Diffenbaugh, 2012: Influenceof twenty-first-century atmospheric and sea surface temperature forcing on West African climate. J. Climate, 25, 527–542,
doi:10.1175/2011JCLI4183.1.
Sud, Y. C., G. K. Walker, and K.-M. Lau, 1999: Mechanisms regulating sea-surface temperatures and deep convection in thetropics. Geophys. Res. Lett., 26, 1019–1022, doi:10.1029/
1999GL900197.
Tang, Y., L. Li, W. Dong, and B. Wang, 2016: Tracing the source of
ENSO simulation differences to the atmospheric component of
two CGCMs. Atmos. Sci. Lett., 17, 155–161, doi:10.1002/asl.637.
Trenberth, K. E., and D. J. Shea, 2005: Relationships between
precipitation and surface temperature. Geophys. Res. Lett., 32,
L14703, doi:10.1029/2005GL022760.
Waliser, D. E., and N. E. Graham, 1993: Convective cloud systems
and warm-pool sea surface temperatures: Coupled interactions and self-regulation. J. Geophys. Res. Atmos., 98,
12 881–12 893, doi:10.1029/93JD00872.
Wang, B., Q. Ding, X. Fu, I.-S. Kang, K. Jin, J. Shukla, and
F. Doblas-Reyes, 2005: Fundamental challenge in simulationand prediction of summer monsoon rainfall. Geophys. Res.
Lett., 32, L15711, doi:10.1029/2005GL022734.
Wu, R., B. P. Kirtman, and K. Pegion, 2006: Local air–sea relationship in observations and model simulations. J. Climate,
19, 4914–4932, doi:10.1175/JCLI3904.1.
Zhang, H., A. Clement, and P. Di Nezio, 2014: The South Pacificmeridional mode: A mechanism for ENSO-like variability.
J. Climate, 27, 769–783, doi:10.1175/JCLI-D-13-00082.1.
Zhang, R., 2008: Coherent surface-subsurface fingerprint of theAtlantic meridional overturning circulation. Geophys. Res.
Lett., 35, L20705, doi:10.1029/2008GL035463.
——, R. Sutton, G. Danabasoglu, T. L. Delworth, W. M. Kim,
J. Robson, and S. G. Yeager, 2016: Comment on ‘‘The Atlantic
Multidecadal Oscillation without a role for ocean circulation.’’
Science, 352, 1527, doi:10.1126/science.aaf1660.
Zwiers, F. W., and H. von Storch, 1995: Taking serial correlation
into account in tests of the mean. J. Climate, 8, 336–351,
doi:10.1175/1520-0442(1995)008,0336:TSCIAI.2.0.CO;2.