**Mid-latitude excitation of tropical variability in the Pacific: the
role of thermodynamic coupling and seasonality**

Daniel J. Vimont

Department of Atmospheric and Oceanic Sciences and Center for Climatic
Research, University of Wisconsin -- Madison, Madison, WI

Michael Alexander

NOAA Earth Systems Research Laboratory, Boulder, CO

Abigail Fontaine

Department of Atmospheric and Oceanic Sciences and Center for Climatic
Research, University of Wisconsin -- Madison, Madison, WI

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

*Corresponding author address:* Daniel J. Vimont, 1225 W. Dayton St.,
Madison, WI 53706.

E-mail: <dvimont@wisc.edu>

ABSTRACT

A set of ensemble model experiments using the National Center for
Atmospheric Research Community Atmospheric Model v. 3.1 (CAM3) are run
to investigate the tropical Pacific response to mid-latitude atmospheric
variability associated with the atmospheric North Pacific Oscillation
(NPO). Heat flux anomalies associated with the NPO are used to force a
set of model simulations during boreal winter (when the NPO is most
energetic), after which the forcing is switched off and the coupled
model evolves on its own. Sea surface temperature (SST) and wind
anomalies continue to amplify in the tropical Pacific after the imposed
forcing has been shut off, indicating that coupled ocean-atmosphere
interactions in the tropical Pacific alter the spatial and temporal
structure of variability associated with mid-latitude forcing. The
tropical circulation evolves through feedbacks between the surface wind,
evaporation, and SST (the WES feedback), as well as through changes in
the shortwave radiative heat flux (caused by changes in convection).

Sensitivity experiments are run to investigate how thermodynamic
coupling and seasonality affect the tropical response to NPO-related
forcing. Seasonality is found to affect the WES feedback through *(i)*
altering the sensitivity of surface evaporation to changes in the
low-level wind field, and *(ii)* altering the structure and strength of
the lower-level wind response to SST anomalies. Thermodynamic coupling
causes an equatorward and westward development of SST anomalies and an
associated equatorward shift in the lower-level zonal wind anomalies.

**1. Introduction**

Interannual and decadal variations in the tropical Pacific associated
with the El Niño -- Southern Oscillation (ENSO) and ENSO-like decadal
variability (Zhang et al. 1997) are closely tied to mid-latitude
atmospheric variability both through tropical influences on the
mid-latitudes via the "atmospheric bridge" (Alexander 1992a, b; Lau
1997; Trenberth et al. 1998; Alexander et al. 2002) and through
mid-latitude atmospheric influence on the tropical circulation (Barnett
et al. 1999; Pierce et al. 2000; Vimont et al. 2001, 2003a, b; Vimont
2005; Alexander et al. 2007). In this study we investigate the dynamics
of the latter connection -- the mid-latitude influence on tropical
variability. In particular, we investigate how a dominant pattern of
atmospheric variability in North Pacific, the North Pacific Oscillation
(NPO; Walker and Bliss, 1932; Rogers, 1981; Linkin and Nigam, 2007)
excites coupled dynamical feedbacks in the subtropical and tropical
Pacific.

The role of mid-latitude atmospheric variability in exciting tropical
coupled variations has been demonstrated in both the Pacific and
Atlantic basins. In the Atlantic, trade wind variations associated with
the North Atlantic Oscillation (NAO) alter subtropical SST through
changes in surface latent heat flux (Xie and Tanimoto 1998; Czaja et al.
2002). The resulting SST anomalies excite a tropical response that
includes a shift in the intertropical convergence zone (ITCZ) and
cross-equatorial boundary-layer flow toward the warmer hemisphere
(Hastenrath and Heller 1977). An important component of the lower-level
flow includes a veering of the winds to the right (left) in the Northern
(Southern) Hemisphere, in accord with the Coriolis force, that results
in a relaxation (intensification) of the lower-level trades in the
warmer (colder) hemisphere. These trade wind fluctuations change
evaporation leading to a positive feedback on the original SST anomaly.
This positive feedback between wind, evaporation, and SST (the WES
feedback; Xie and Philander 1994; Chang et al. 1997) has been shown to
play an important role in tropical "meridional mode" variations in the
Atlantic in observations (Czaja et al. 2002, Chiang and Vimont 2004), in
theoretical analyses (Xie 1997, 1999), and more complete coupled models
(Carton et al. 1996; Chang et al. 1997; Chang et al. 2000).

Mid-latitude variability associated with the NPO in the Pacific appears
to excite a similar sequence of variations in the subtropical and
tropical Pacific (Vimont et al. 2001, 2003a, b; Chiang and Vimont 2004;
Chang et al. 2007). The NPO-forced subtropical SST anomalies excite an
atmospheric circulation that includes zonal wind anomalies that extend
into the deep tropics; these wind anomalies are responsible for exciting
or altering subsequent ENSO events (Barnett et al. 1999; Pierce et al.
2000; Anderson 2003; Vimont et al. 2003a, b).

The excitation of tropical variability by mid-latitude processes is
regulated by the seasonality of mid-latitude variability (the forcing
process) and seasonality associated with the coupled response. The
seasonality of tropical Atlantic variability is strongly influenced by
the boreal winter NAO variations, as well as the seasonal response to
ENSO (Chang et al. 2000; Czaja et al. 2004). In the Pacific, the
seasonality of mid-latitude atmospheric variability can affect ENSO's
phase-locking to the seasonal cycle (Chang et al. 2007). This suggests
an important role for mid-latitude seasonality in altering tropical
Pacific variability. The WES feedback itself involves three different
processes that are sensitive to the mean state and seasonal cycle: *(i)*
wind variations generate changes in surface evaporation (e.g.
evaporation changes depend on the mean wind speed), *(ii)* surface
evaporation alters the underlying SST (e.g. the amount of SST change
depends on the mixed layer depth, which varies seasonally), and *(iii)*
the resulting SST anomalies generate atmospheric circulation anomalies
that, if the WES feedback is to be positive, should resemble the
original wind variations \[the atmospheric response depends on the mean
ITCZ location; e.g. Okajima et al. (2003)\]. In the present study, we
focus on how seasonality affects the first and third processes (section
5, below).

In this study we run a set of coupled and uncoupled general circulation
model experiments with prescribed surface flux forcing associated with
the NPO to investigate its potential influence on the tropical
circulation. The model and experimental design are explained in Section
2, and the imposed NPO-related forcing is described in Section 3. The
model response to imposed NPO heat flux forcing is described in Section
4, followed by a discussion of seasonality and the WES feedback in the
Pacific in Section 5. Finally, a discussion of results is presented in
Section 6.

**2. Model description and experimental design**

*a. Model description*

Model experiments are conducted using the National Center for
Atmospheric Research Community Atmospheric Model, version 3.0 (CAM3;
Collins et al. 2006). The model is run at T42 resolution (approximately
2.8 resolution in physical space), with 26 vertical levels and a 20min
time step. The surface sensible and latent heat flux is calculated using
a standard bulk formula (see section 4c) that includes a
stability-dependent transfer coefficient. CAM3 can be run in an
uncoupled setting by imposing the observed climatological seasonal cycle
as a lower boundary condition. We obtained 200yr of data from a long
uncoupled control simulation (CAM3 control; Table 1) that had been run
at NCAR by A. Phillips. A set of sensitivity experiments are also run in
which the SST climatology is altered by imposing SST anomalies in the
eastern subtropical Pacific (section 5).

For most experiments in the present study, CAM3 is coupled to a uniform
depth, motionless, 50m slab ocean model (SOM), which allows the coupled
system to generate its own SST variability. The SOM represents a uniform
(well mixed) oceanic mixed layer that determines SST through integrating
the net surface heat flux. To account for oceanic heat transports and
model biases a flux correction (Q-flux) is applied to the SOM. The
Q-flux applied at each time step is a linear interpolation of
mid-monthly values from the current and closest surrounding months (e.g.
the Q-flux for November 1 would be a weighted average of the October and
November Q-flux). A control simulation of the CAM3+SOM is run for 20
years, of which the last 10 years are used to generate initial
conditions for the ensemble experiments, and for analysis.

*b. Experimental Design*

To separate the effect of mid-latitude forcing from tropical coupled
processes we run ensembles of model simulations. Three experiments are
conducted: *(i)* a boundary forced experiment in which the NPO-related
surface heat flux is imposed as a forcing to the CAM3+SOM, *(ii)* a
boundary forced experiment in which an idealized SST anomaly is used to
force the uncoupled CAM3 model, and *(iii)* an experiment in which the
same idealized SST (heat content) anomaly is used to initialize a set of
CAM3+SOM simulations. For reference all experiments in the present study
are summarized in Table 1 and described further below.

The NPO-forced experiments are conducted as follows. During boreal
winter (NDJFM) when the NPO is most energetic, the oceanic mixed layer
is driven by an external forcing by adding the NPO-related heat flux
anomaly to the Q-flux (Figure 1b; described below). The imposed forcing
is turned off in April and the model variability evolves via its own
coupled dynamics and feedbacks. Due to the linear interpolation from the
monthly Q-flux field to the model's 20min time step, the imposed forcing
gradually increases to full strength by mid-November, and gradually
decreases from full strength in mid-March to zero amplitude by
mid-April. The model is then run without any imposed forcing through the
following April (18 months total). We note that the experimental
formulation may include redundancy between imposed NPO-related heat flux
and internally generated NPO variability in any individual model
simulation. To minimize the influence of internally generated
variability we run an ensemble of model simulations so that averaging
across the individual members, each of which should contain internal
variability that is incoherent with other ensemble members, will
emphasize the NPO-forced model response. Although this linear assumption
is an over-simplification the coherent model response throughout the
study is taken as evidence that the experimental design is appropriate
for addressing the NPO's influence on tropical climate variability.

In Section 5 we investigate the role of seasonality and the WES feedback
by running ensemble simulations using an idealized SST anomaly as a
forcing for the uncoupled CAM3 (an uncoupled, 10-year simulation with
constant forcing; "CAM3 SST-forced" in Table 1), or as an initial heat
content anomaly for the CAM3+SOM (an "initial value problem"; IVP in
Table 1). The idealized SST or heat content anomalies have a gaussian
shape in the meridional direction with an e-folding width of 7.5° and a
cosine shape in the zonal direction with a width of 60° centered at
22.5°N, 150°W, with a maximum amplitude corresponding to 1°C. This shape
and amplitude is motivated by the response to the NPO-forced CAM3+SOM
experiments (Figure 2). For the IVP experiment, we run four experiments
initialized with the idealized heat content anomalies in April, July,
October, and January (Table 1).

Two ensembles of model simulations are run for each experiment,
corresponding to positive or negative polarity of the forcing or initial
condition. To reduce sampling error, each ensemble contains ten
individual model simulations initiated from different initial conditions
(taken from each November 1 during the last ten years of the CAM3+SOM
control simulation). The heat flux anomalies in the NPO-forced
experiments (defined below) are those that would correspond to a two
standard deviation anomaly of the NPO (thus, the heat flux anomalies in
Figure 1b are multiplied by two). By convention, experiments with
positive (negative) heat flux forcing or positive (negative) SST
anomalies in the eastern subtropical Pacific are labeled WARM (COLD)
experiments, and downward heat flux is positive. Results are presented
as one half the difference between the WARM and COLD ensemble averages.
Unless otherwise stated, results discussed in the present analysis are
generally statistically significant at the 95% confidence level using a
2-tailed Students T-test.

**3. Uncoupled NPO-related heat flux forcing**

The imposed NPO-related forcing (NPO-WARM and NPO-COLD experiments;
Table 1) is designed to represent heat flux anomalies associated with
the NPO in the absence of coupling. In a coupled system the net heat
flux contains contributions from atmospheric forcing and the response to
that forcing. We use two different methods to estimate the atmospheric
contribution to the NPO-related heat flux forcing, the results of which
are compared in Figure 1. The first method uses existing observations
from the NCEP-NCAR reanalysis project (Kalnay et al. 1996), and the
differing time scales of oceanic and atmospheric variations. We apply an
*intra*-seasonal filter to distinguish between the atmospheric (higher
frequency) and oceanic (lower frequency due to the thermal inertia of
the oceanic mixed layer) contributions to the net surface heat flux. In
the second method, the atmospheric contribution to the heat flux is
defined in the long uncoupled CAM3 control simulation (Table 1), which
includes no oceanic contribution as the SST is fixed to climatology.

The NPO is defined from the NCEP reanalysis by applying empirical
orthogonal function / principal component (EOF / PC) analysis to the
*intra*-seasonal boreal winter (NDJFM) SLP anomalies over the North
Pacific (110E-70W, 20N-90N) and over the time period 1948-2002. The
intra-seasonal data is generated by: (i) removing the annual cycle from
the SLP data by subtracting the mean for each calendar month, then (ii)
subtracting each winter's mean from the corresponding monthly data.
Although this does not eliminate all coupled variability, it
significantly reduces the interannual variability associated with ENSO
or with local coupled ocean-atmosphere interactions. The second EOF (19%
variance explained) represents variability of the NPO, and is well
separated from the first EOF (the Aleutian Low, 26% variance explained)
and the higher order EOFs (11% variance explained or less). Finally, the
intra-seasonal net surface heat flux is regressed onto the second PC
time series to obtain the net surface heat flux variability associated
with one standard deviation of the intra-seasonal NPO. Regression maps
of intraseasonal SLP and heat flux anomalies onto the observed
standardized second PC time series are plotted in Figure 1a.

The second method defines the NPO in the long (200-yr) uncoupled CAM3
control simulation. EOF analysis is applied to the winter (NDJFM)
averaged SLP anomalies (over the same region), and the net heat flux is
regressed onto the second PC time series (26% variance explained; the
leading EOF represents the Aleutian Low, and explains 35% of the
variance; higher order modes explain 11% or less). Regression maps of
SLP and heat flux onto the model's standardized second PC time series
are plotted in Figure 1b. The amplitudes of both maps may be directly
compared as they represent the amplitude of anomalies per standard
deviation of the associated NPO time series.

Both the modeled and observed NPO share similar structures in their SLP
and heat flux maps, and bear a strong resemblance to analysis of the
second EOF of maritime SLP and its associated heat flux by Cayan (1992)
using a different data set. An important feature of the SLP maps is a
low pressure anomaly centered around 35-40N. The surface wind anomalies
along the southern flank of this SLP anomaly reduce the climatological
trade wind strength, and consequently reduce the upward turbulent heat
flux from the ocean surface \[this turbulent heat flux is dominated by
the latent heat flux; see also Cayan (1992)\]. In both the model and
observations, a zonally elongated band of positive (downward) heat flux
anomalies extends across the subtropical and tropical Pacific, from
around 150E to the eastern edge of the basin. These heat flux anomalies
extend far enough equatorward to have an important effect on the
tropical climate. In the mid-latitudes, a band of negative (upward) heat
flux anomalies extends from the west into the central North Pacific.

Despite the similarities between the observed and modeled variability,
there are subtle differences in the NPO structure. In particular, the
modeled NPO structure has a more hemispheric signature representative of
the northern annular mode (NAM); the modeled annular mode is known to be
overly coherent between the Pacific and Atlantic sectors. We note that
the observed NPO does not resemble the observed NAM over the Pacific
sector, and indeed is nearly orthogonal to the observed NAM (NAM
variations in the Pacific sector more closely resemble variations in the
Aleutian low, which are orthogonal to the NPO by construction).
Differences in the net surface heat flux also exist that may affect
details of the NPO's impact on tropical Pacific climate variability.
Most notably, the anomalously low SLP in the model, and the
corresponding subtropical heat flux anomalies, are centered about 5
north of the corresponding anomalies in the observational record.
Differences may result from model error or differences in analysis
techniques. We ran the NPO-WARM and NPO-COLD experiments with both heat
flux anomalies, and found that results did not substantially differ. For
consistency, results from the CAM3-generated heat flux anomaly pattern
in Figure 1b are shown in the present study.

**4. Model response to NPO-related heat flux**

*a. Description of the model response*

The modeled response to the imposed net surface heat flux anomaly in
Figure 1b is shown for various seasons in Figure 2. Model lower-level
winds (defined as the mass-weighted average below 850mb) are only shown
where statistically significant at the 95% level based on a multivariate
t-test. During late boreal winter \[JFM (year 1); Figure 1a\], the SST
anomalies bear a strong resemblance to the imposed heat flux forcing
during the winter, suggesting that the ensemble size is sufficient to
eliminate much of the random model variability. Lower level winds are
weak at this time. Not surprisingly, the largest SST anomalies appear in
eastern subtropics (around 20N, 150W), in the same location as the
maximum downward heat flux anomalies (c.f. Figure 1). These subtropical
SST anomalies develop through boreal winter and maximize in boreal
spring consistent with the phasing implied by integration of the imposed
forcing (Hasselmann 1976), then decay through the remainder of the model
simulation.

By boreal spring (AMJ; Figure 2b), positive SST anomalies have spread
into the northwestern tropical Pacific region (NWTP, 120E-180W, 0-15N,
box in Fig. 2a) despite weak *negative* (upward) heat flux anomalies
imposed in that region from November through March (though subtle, these
are indicated by dark shading in Figure 1b). This indicates that coupled
feedbacks associated with the model response are acting to increase SST
in that region during the imposed forcing period. The lower level wind
field shows a cyclonic circulation centered around 165E and 20N,
including a zonal band of westerly anomalies in the NWTP. By boreal
summer (JAS; Figure 2c) SST anomalies in the NWTP have slightly
amplified despite the removal of the imposed heat flux anomalies during
the previous season. This amplification occurs in the region of relaxed
lower-level trade winds, highlighting the role of coupled feedbacks in
the evolution of tropical SST anomalies in the tropical Pacific.

Tropical SSTs continue to decay, though slowly, through boreal fall and
into winter, as demonstrated in Figure 2d and 2e. Of interest is the
persistence and slight increase of SST anomalies around 10N between the
dateline and 150W through boreal fall (OND; Figure 2d); and the
development of two bands of negative SST anomalies near the date line
and the equator in fall, and near 20S and 165W during fall and the
following winter. The positive SST anomalies are collocated with
westerly wind anomalies, and the two negative SST anomalies are
collocated with increased easterly winds. Again, this suggests an
important role for coupled feedbacks. During the winter \[JFM (year 2);
Figure 1e\] SST and atmospheric circulation anomalies in the northern
tropics continue to decay.

*b. Heat budget in the NWTP*

To better understand the physical processes leading to the development
of the NWTP SST anomalies, the heat budget for this region is plotted in
Figure 3. Note that in the SOM, the only process that can affect the
anomalous SST is the net surface heat flux, comprising the net shortwave
and longwave radiation, the turbulent sensible and latent heat fluxes,
and any imposed forcing. Of these terms, it is found that only the
latent and shortwave heat fluxes contribute appreciably to the heat
budget.

Figure 3 shows that SST anomalies in the NWTP involve a period of
amplification (Feb -- Jun), and two periods of decay (Jun -- Sep, and
Nov onward) punctuated by a period of sustained SST (Sep -- Nov). The
amplification of SST anomalies during the Feb -- Jun period occurs due
to positive contributions from the latent and shortwave fluxes. During
the latter half of this amplification (Mar -- Jun) lower level wind
anomalies are opposed to the mean winds (Figure 3, bottom; note that for
plotting purposes, anomalous winds are scaled four times larger than
mean winds in Figure 3), implying a relaxation of the climatological
trade wind strength and a reduction in the upward latent heat flux (a
positive latent heat flux anomaly). This relationship demonstrates a
positive WES feedback during this period of development. Shortwave heat
fluxes also contribute to the boreal spring warming.

The cooling periods (Jun -- Sep, and Nov onward) are caused mainly by
negative shortwave heat flux anomalies (reduced downward shortwave
radiation), with a small contribution from negative latent heat flux
anomalies during boreal summer. It is noteworthy that the boreal summer
(Jul - Sep) latent heat flux anomaly decreases considerably from the
previous season, despite *stronger* lower-level wind anomalies in the
NWTP region. This suggests that WES does not exert as strong a positive
feedback over the NWTP region during boreal summer. During boreal fall
(Sep -- Nov) both latent and shortwave heat flux anomalies are near
zero, despite warmer SST over the NWTP region. Again, the near-zero
latent heat flux anomalies occur during a period of relaxed westerly
wind anomalies. The varying strength of the latent heat flux in Figure
3, and its non-uniform relationship with the mean winds, suggests that
the seasonal cycle plays an important role in altering the strength of
the WES feedback in the tropical Pacific (Section 5).

*c. Surface heat flux components and the WES feedback*

The importance of coupled feedbacks in the evolution of SST anomalies in
Figure 3 motivates further analysis of the physical processes that
contribute to the net surface heat flux. The net surface heat flux
anomalies during boreal spring (AMJ) and summer (JAS) are contoured in
Figure 4a and 4b, respectively. For reference, underlying SST anomalies
are shaded. In both of these seasons, the net surface heat flux tends to
damp underlying SST anomalies over most of the subtropics. One exception
is the NWTP region during boreal spring, where large downward heat flux
anomalies are collocated with warm SSTs. During summer, heat flux
anomalies in the NWTP generally act to damp underlying SSTs, though
there is a region of downward heat flux over warm SST near the dateline.

The shortwave and latent heat flux contributions to the net surface heat
flux are contoured in the second and third rows of Figure 4,
respectively. Precipitation anomalies are shaded under the shortwave
contours in Figures 4c and d. In general, the shortwave heat flux
follows changes in cloud amount, which can also be inferred from
precipitation. During spring, the western tropical Pacific is
characterized by a northward ITCZ shift, which corresponds to a
cloudiness reduction and redistribution over the NWTP region (downward
shortwave anomalies accompanying reduced precipitation). In summer,
precipitation anomalies are more disorganized (the mean ITCZ is also
less confined in the western Pacific during summer) though there is a
general increase in precipitation and decrease in downward shortwave
radiation over the entire western subtropical Pacific. The model latent
heat flux is contoured in Figures 4e and f. During spring, the latent
heat flux is characterized by a large negative anomaly over the eastern
subtropical Pacific (where the spring SST is at a maximum) and a
positive anomaly over the NWTP region, where westerly wind anomalies are
largest (Figure 4g and 2b). We note that the positive (downward) net
surface heat flux anomalies over the NWTP region involve equal
contributions from the latent and shortwave heat fluxes.

Latent heat flux anomalies will result from changes in the surface wind
speed and from changes in the vertical humidity gradient. We infer
contributions from both processes by examining the contribution of zonal
wind, or of the specific humidity gradient, to the total latent heat
flux as follows. The bulk formula for calculating latent heat flux
(*LH*) may be written:

![](media/image1.png){width="3.348611111111111in"
height="0.18958333333333333in"}, (1)

where *L~v~* is the latent heat of vaporization (*2.510^6^* *J kg^-1^*),
*C~e~* is an aerodynamic transfer coefficient (a function of stability
and wind speed in the model; we use a constant value of *10^-3^*), *~a~*
is the density of air (*1.2 kg m^-3^*), *q~sat~(T)* is the saturated
specific humidity at temperature *T*, *RH* is the relative humidity at
the lowest model level (about 65m above the surface; relative humidity
varies by only a few percent over the bottom 250m), *T~s~* is the
surface temperature, *T~ref~* is the atmospheric temperature at a
reference height of 2m, and $\overline{w}$ is the wind speed. The wind
speed is calculated as:

![](media/image2.png){width="1.7479166666666666in"
height="0.24791666666666667in"} (2)

where *u* and *v* are the monthly averaged zonal and meridional
components of the wind, and $\hat{w}$ is a background wind speed used to
account for sub-monthly wind variance \[see, e.g., Czaja et al. (2002);
we set $\hat{w}$ equal to 4 m s^-1^\]. Wind speed $\overline{w}$ is
defined using the \>850mb vertically averaged wind components. In
reality, the bulk formula would use wind speed at a reference height
(say, 10m), so we scale the \>850mb vertically averaged wind speed by
0.8 to account for the reduction of wind speed near the surface. Monthly
averaged model data from the CAM3+SOM control run and the NPO-WARM and
NPO-COLD boundary forced experiments are available for calculating the
bulk latent heat flux. The use of monthly mean data in the non-linear
equations for wind speed and saturated specific humidity implies that
results are approximate.

The model response in the NPO-forced simulations includes *(i)* changes
in the surface winds that affect the latent heat flux through enhanced
vertical transport, and *(ii)* changes to the sea surface temperature
that are expected to affect the latent heat flux through changing the
vertical humidity gradient. To isolate *(i)* the effect of zonal wind on
the latent heat flux (contours in Figure 4g and h) we use data from the
CAM3+SOM control simulation for all parameters in equations (1) and (2)
except the zonal wind (*u*), which is taken from either the NPO-WARM or
NPO-COLD boundary forced simulation. Similarly, *(ii)* the contribution
of the vertical humidity gradient to the latent heat flux (contours in
Figure 4i and j) is identified using data from the CAM3+SOM control
simulation for calculating the wind speed (Eqn. 2), and using data from
the NPO-WARM or NPO-COLD boundary forced experiment to calculate the
vertical humidity gradient (*T~s~*, *T~ref~*, and *RH*). The
contribution from changes in the meridional wind speed is very small.
The sum of estimated contributions from changes in the zonal and
meridional winds and changes in the vertical humidity gradient are
shaded in Figure 4e and f, and show close correspondence with the actual
latent heat flux anomalies (contours in Figure 4e and f) from the
simulations.

Figures 4g and 4i show that during boreal spring the downward latent
heat flux anomalies in the NWTP region are caused by the wind anomalies
(westerly wind anomalies overly downward latent heat flux anomalies) in
the NPO-forced simulations. The contribution of zonal wind anomalies to
the latent heat flux in the NWTP region is opposed by changes in the
vertical humidity gradient. In general, increases in SST tend to
increase the vertical humidity gradient and upward latent heat flux,
thus countering the downward latent heat flux anomalies generated by the
zonal wind. The eastern subtropics are characterized by large SST
anomalies but weak wind anomalies. There, changes in the vertical
humidity gradient dominate the latent heat flux anomalies (Figure 4e).
The positive contribution of zonal wind anomalies to the latent heat
flux illustrate the importance of wind-induced evaporation and the WES
feedback on the development of SST anomalies in the NWTP in response to
the NPO.

During the summer the opposing effects of the wind speed and vertical
humidity gradient are closer in magnitude (Figures 4h and 4j). This
results in a less clearly defined structure for the total latent heat
flux (Figure 4f). An interesting feature during boreal summer is seen in
the far western tropical Pacific (around 15N, 120E) where westerly zonal
wind anomalies (light shading) are associated with very weak and even
*upward* latent heat flux anomalies. Furthermore, the large tropical
westerly wind anomalies near 15N, 150E (Figure 4h and 2c) do little to
reduce the mean upward latent heat flux from the surface. These features
will be discussed further in Section 5.

**5. Seasonality and the WES feedback**

The WES feedback involves three physical relationships: *(1)* changes in
lower level winds alter surface evaporation, *(2)* evaporation generates
SST anomalies, and *(3)* the SST anomalies affect the lower level winds.
In the present model formulation the second physical relationship, the
link between evaporation and SST, is necessarily one in which reduced
(increased) evaporation will warm (cool) the surface in a consistent
manner. This leaves two physical processes that may be influenced by
seasonality in the present model: the sensitivity of surface evaporation
to changes in the lower-level wind, and the sensitivity of lower-level
winds to the development of SST anomalies. For the WES feedback to be
positive, warm (cold) SST anomalies need to generate a local relaxation
(intensification) of the mean wind speed (e.g. Xie 1999).

*a) The seasonal cycle of latent heat flux sensitivity*

The effect of the mean state on the surface latent heat flux can be
diagnosed by linearizing the bulk latent heat flux formula (Equation 1):

![](media/image3.png){width="3.1493055555555554in"
height="0.3902777777777778in"} (3)

Each of the partial derivatives indicates the sensitivity of the latent
heat flux to a unit change in the respective field. We focus on changes
in the zonal wind speed and surface temperature, as the latent heat flux
is only weakly sensitive to changes in the meridional wind (not shown).
The linear assumption in Eq. (3) is clearly an oversimplification, and
as such is used for illustrative purposes here.

The sensitivity of the latent heat flux anomalies to changes in the
zonal wind speed is:

![](media/image4.png){width="4.317361111111111in"
height="0.3923611111111111in"} (4)

Over the tropical oceans this is negative everywhere where there are
mean easterlies implying that a westerly wind anomaly (positive *u* in
Equation 4) will act to decrease the mean upward latent heat flux, and
warm the surface. Larger *mean* easterlies will imply a larger downward
(upward) anomalous heat flux for the same *anomalous* westerly
(easterly) zonal wind speed. The sensitivity of the latent heat flux to
changes in the surface temperature can be inferred by noting that in
general over the ocean *T~ref~ \< T~s~*,
$¶\frac{T_{\text{ref}}}{¶}T_{s}»1$, *RH \< 1*, and changes in relative
humidity are small. Then, the exponential dependence of the
Clausius-Clayperon equation implies that an increase in *T~s~* will
cause a larger change in *q~sat~(T~s~)* than *q~sat~(T~ref~)*, and hence
an increase in the vertical humidity gradient. Thus, all else being
equal, positive (negative) SST anomalies will tend to increase
(decrease) evaporation -- a negative feedback.

The zonal wind sensitivity is plotted with the mean winds from the
CAM3+SOM control experiment in Figure 5 (polarity is reversed so that
positive values indicate a warming of the surface for a westerly wind
anomaly). The zonal wind sensitivity demonstrates an important role of
the mean state in affecting the strength of latent heat flux feedbacks
in the NWTP region. During boreal spring and fall (Figure 5a and 5c) the
sensitivity is uniformly large (15-20 W s m^-3^) over most of the
tropics consistent with relatively uniform easterly trades over the
region. During boreal summer and winter (Figures 5b and 5d) two regions
of reduced, and even negative, sensitivity emerge in the western summer
hemisphere tropics (\~120W-135W, 7.5N-22.5N in JAS; or west of 180 and
between \~7.5S-15S during JFM), due to near-zero or westerly mean wind
anomalies. This reduction in the zonal wind sensitivity explains the
weak negative latent heat flux anomalies in the NWTP region during
boreal summer despite the existence of mean westerly wind anomalies
(Figure 3; Figure 4h).

The key features of the seasonal cycle that influence this sensitivity,
and hence the development of SST anomalies in the NWTP region, may be
inferred by examining streamlines of the lower-level wind field over the
tropical Pacific and Indian oceans in Figure 6. During April, easterly
winds extend across the entire Pacific associated with a subtropical
high that extends to the western edge of the basin. By July (Figure 6b)
the subtropical high and monsoon trough have migrated to their
northernmost positions (around 40N, 210E; and 20N, 150E, respectively;
Hastenrath 1991; Ramage 1995), associated with a region of weak, and
even westerly, mean winds between 120-150E, 10-25N. Figure 5b shows that
the northward-shifted monsoon trough is related to a reduction, and even
reversal in sign, of the latent heat flux sensitivity to zonal wind
anomalies. Note that similar features are found in the Southern
Hemisphere around 15S, 165E, corresponding to the austral summer monsoon
(compare Figure 5b with 5d). The sign reversal of the latent heat flux
sensitivity demonstrates the importance of the mean seasonal cycle in
affecting the evolution of coupled tropical variability associated with
the WES feedback. In fact, a similar analysis of observations (using the
NCEP / NCAR reanalysis; not shown) indicates that the simulated monsoon
winds are weaker than observed; the corresponding observed latent heat
flux sensitivity to zonal wind anomalies is larger (and negative) in
nature than in the model.

*b) Seasonality of the low-level wind response*

In the coupled model experiments, SST anomalies are generated by the
imposed heat flux forcing (the directly forced response) and through the
coupled response to these directly forced SST anomalies (the coupled WES
feedback response). To distinguish the directly-forced response from the
internally generated response we compare the uncoupled "CAM3 SST-forced"
and coupled "IVP" experiments (Table 1; Section 2) in which an idealized
SST anomaly is imposed in the eastern subtropical Pacific (Figure 8a).
The uncoupled "CAM3 SST-forced" experiment illustrates the atmospheric
response to (idealized) NPO-generated SST anomalies in the absence of
coupled feedbacks. We deliberately focus on forcing from the eastern
subtropics to isolate the coupled response in the NWTP region. The IVP
simulations impose a heat content anomaly as an initial condition to the
CAM3+SOM model, and as such the atmospheric response includes both the
uncoupled response to the idealized NPO SST, as well as the coupled
response that evolves through the WES feedback. The coupled response
(due to the WES feedback) can be inferred by comparing the uncoupled
CAM3 SST-forced experiment with the CAM3+SOM experiment (Figure 8). In
Figure 8 we show results for boreal summer only, though the main
findings are similar during other seasons.

The lower-level zonal wind and net surface heat flux response in the
uncoupled CAM3 SST-forced simulation is shown in Figure 7. These plots
demonstrate that the zonal wind response is sensitive to the seasonal
cycle. In general, westerly zonal wind anomalies develop to the
southwest of the imposed SST anomaly, with the largest amplitude during
boreal summer and fall (Figure 7b and 7c), and weakest amplitude during
boreal spring and winter (Figure 7a and d). These westerly wind
anomalies are collocated with reduced evaporation, which contributes to
a downward net surface heat flux. The large zonal wind response in the
western Pacific (around 25N, 120E-180) during boreal summer is centered
much farther poleward than the response in other seasons. In the far
western NWTP region the boreal summer response includes *easterly* lower
level wind anomalies, which, when combined with the weakly negative
zonal wind sensitivity in Figure 5b, actually *reduces* the upward
latent heat flux from the surface. This implies that the WES feedback is
still positive, despite a negative latent heat flux sensitivity to the
zonal wind anomalies in the NWTP region. A comparison of Figures 5b, 6b,
and 7b suggests that the monsoon trough affects both surface evaporation
and the structure of atmospheric wind anomalies in the NWTP region.

The uncoupled CAM3 experiments demonstrate that SST anomalies in the
subtropical Pacific are capable of directly forcing a wind and heat flux
response in the western and central tropical Pacific. Next, we
investigate how coupled processes associated with the WES feedback
affect these wind anomalies. Wind and SST anomalies are plotted for the
months of Jul -- Sep in the uncoupled CAM3 SST-forced experiment (Figure
8a), the July IVP experiment (Figure 8b; corresponding to months 1-3 of
the July IVP ensemble experiment), and from the April IVP experiment
(Figure 8d; corresponding to months 4-6 of the April IVP ensemble
experiment) in the left column of Figure 8. For reference, differences
between the panels (July IVP minus uncoupled CAM3 SST-forced simulation,
and April IVP minus July IVP) are plotted in Figures 8c and e,
respectively. Note that all panels depict variations during the Jul --
Sep season.

Figure 8a shows the directly forced (no coupled feedbacks) wind response
during boreal summer in the uncoupled CAM3 SST-forced simulation. As
discussed earlier, this wind pattern (in its plotted polarity) would
tend to generate warm SST anomalies in the NWTP region. This is
consistent with the band of warm SST anomalies that develops in Figure
8b, and the difference map in Figure 8c. Panels 8b and 8c also show that
the zonal wind structure in the CAM3+SOM July IVP experiment (which
includes coupled feedbacks) is shifted southward and slightly westward
of its counterpart in the CAM3 SST-forced simulation (which has no
coupled feedbacks). The same southwestward shift in the zonal wind
response is also prominent in months 4-6 of the April IVP experiment in
Figure 8d (compare to Figure 8a). Here, the development of warm SST
anomalies in the NWTP region during the preceding boreal spring excites
westerly wind anomalies that extend down to the equator during boreal
summer. In fact, the development of warm SST anomalies in the NWTP
region (Figure 8d) during the preceding boreal spring appears to enhance
the southwestward shifted zonal wind response, even compared to the July
IVP simulation (Figure 8d and e).

The sequence of plots in Figure 8 demonstrates that coupling plays an
important role in the evolution of variability via the WES feedback in
the NWTP region. In particular, coupling with the ocean allows SST
anomalies to develop southwest of the imposed SST forcing in the NWTP
region. These SST anomalies, in turn, allow lower-level wind anomalies
to develop farther equatorward than they would in the absence of
coupling. Seasonality appears to play a subtle role in the coupled
response as well. The development of warm SST anomalies in the NWTP
region during boreal spring in the April IVP simulation (not shown)
leads to larger SST anomalies in the NWTP region during the following
summer (Figure 8d), and hence shifts the wind response farther
equatorward and westward (Figure 8e). Similar differences are seen in
other months. Although these meridional shifts in the maximum wind
anomalies are somewhat subtle, the relatively small oceanic Rossby
radius of deformation implies that these shifts may have very different
impacts on the equatorial ocean in more physically realistic settings
(i.e. in settings where the ocean is able to dynamically respond to the
forcing).

**6. Conclusions and Discussion**

A series of ensemble model experiments were run to investigate how the
tropical ocean / atmosphere system responds to imposed heat flux forcing
associated with the mid-latitude NPO. The model response demonstrates
that the tropical Pacific circulation is sensitive to forcing from the
mid-latitude atmosphere. The tropical response includes zonal wind
anomalies in the central and western tropical Pacific, associated
changes in the net surface heat flux (through contributions from the
latent and shortwave radiative heat fluxes), and the development of SST
anomalies in the northwestern tropical Pacific (NWTP; 120E-180W, 0-15N).
Analysis of the latent heat flux indicates that the WES feedback plays
an important role in altering the evolution of the response in the NWTP
region. In particular, coupling causes zonal wind anomalies to extend
equatorward and westward relative to uncoupled simulations. Mid-latitude
forcing also induces changes in tropical convection, cloudiness and
shortwave radiation. The amplitude of shortwave radiation and latent
heat flux anomalies are comparable in the NWTP region, and cooperate in
their influence on SST during boreal spring.

In the present model formulation, the mean state and seasonal cycle
affect the strength and structure of the WES feedback in two ways: *(1)*
they can alter the sensitivity of latent heat flux anomalies to changes
in the surface winds, and *(2)* they can alter the way that surface
winds respond to the SST anomalies. It is shown that the surface latent
heat flux is sensitive to the seasonal cycle of lower level winds,
especially in the NWTP region where the seasonal migration of the
monsoon trough induces a seasonal reversal of the mean zonal wind. The
atmospheric response to SST anomalies (SST anomalies that are directly
forced by the NPO, or those that develop through the WES feedback) is
sensitive to the seasonal cycle as well, with the largest zonal wind
anomalies in the western Pacific during boreal summer. Boreal summer,
however, is also the time of weakest latent heat flux sensitivity to
westerly wind anomalies. So, despite the strong atmospheric response to
SST anomalies during boreal summer, the development of SST anomalies in
the NWTP region associated with the WES feedback appears to be most
effective during boreal spring and fall.

Results from the present study indicate that the WES feedback plays an
important role in altering the spatial structure of tropical variability
in the western tropical Pacific. In particular, the WES feedback induces
a southwestward shift of zonal wind anomalies in the NWTP region, which,
though subtle, may play an important role in exciting equatorial ocean
dynamics. Future research will include investigation of the NPO's effect
on the development of ENSO in more complete physical model
configurations (i.e. ocean dynamics).

*Acknowledgments.*

This paper is funded by the National Oceanographic and Atmosphere
Administration under grant NA040AR4310139, and by the Graduate School at
the University of Wisconsin--Madison. NCEP Reanalysis data provided by
the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at
http://www.cdc.noaa.gov.

**References**

Alexander, M. A., 1992a: midlatitude atmosphere-ocean interaction during
El Niño. Part I: the North Pacific Ocean.

Alexander, M. A., 1992b: Midlatitude atmosphere-ocean interaction during
El Niño. Part II: the Northern Hemisphere atmosphere. *J. Climate*,
**5**, 959-972

Alexander, M. A., I. Bladé, M. Newman, J. R. Lanzante, N.-C. Lau and J.
D. Scott, 2002: The atmospheric bridge: the influence of ENSO
teleconnections on air-sea interaction over the global oceans. *J.
Climate*, **15**(16), 2205--2231.

Alexander, M. A., L. Matrosova, C. Penland, J. D. Scott, and P. Chang,
2007: Forecasting Pacific SSTs: Linear Inverse Model Predictions of the
PDO. *J. Climate, Accepted.*

Anderson, B.T., 2003: Tropical Pacific sea-surface temperatures and
preceding sea-level pressure anomalies in the subtropical North Pacific,
*J. Geophys. Res.* **108** (D23)

Barnett, T. P., D. W. Pierce, M. Latif, D. Dommenget and R. Saravanan,
1999: Interdecadal interactions between the tropics and midlatitudes in
the Pacific basin. *Geophys. Res. Lett.*, **26**(5), 615--618.

Carton, J. A., X. Cao, B. S. Giese, and A. M. da Silva, 1996: Decadal
and interannual SST variability in the tropical Atlantic Ocean, *J.
Phys. Oceanogr.*, **26**, 1165-1175.

Cayan, D. R., 1992: Latent and sensible heat flux anomalies over the
Northern Oceans: the connection to monthly atmospheric circulation. *J.
Climate,* **5***, 354-369.*

Chang, P., L. Ji, and H. Li (1997), A decadal climate variation in the
tropical Atlantic Ocean from thermodynamic air-sea interactions,
*Nature*, **385**, 516--518.

Chang, P., Saravanan, R., L. Ji and G.C. Hegerl, 2000: The effect of
local sea-surface temperatures on atmospheric circulation over the
Tropical Atlantic sector. *J. Climate*, **13**, 2195-2216

Chang, P., L. Zhang, R. Saravanan, D. J. Vimont, J. C. H. Chiang, L. Ji,
H. Seidel, and M. K. Tippett, 2007: A thermodynamic trigger for El Niño
-- Southern Oscillation. *Geophys. Res. Lett. In Press.*

Chiang, J. C. H., and D. J. Vimont (2004), Analogous Pacific and
Atlantic meridional modes of tropical atmosphere-ocean variability, *J.
Clim., 17*, 4143--4158.

Collins, W.D., C.M. Bitz, M.L. Blackmon, G.B. Bonan, C.S. Bretherton,
J.A. Carton, P. Chang, S.C. Doney, J.J. Hack, T.B. Henderson, J.T.
Kiehl, W.G. Large, D.S. McKenna, B.D. Santer, and R.D. Smith, 2006: The
Community Climate System Model Version 3 (CCSM3). *J. Climate*, **19**,
2122--2143.

Czaja, A., P. van der Vaart, and J. Marshall, 2002: A diagnostic study
of the role of remote forcing in tropical Atlantic variability. *J.
Climate*., **15**, 3280-3290

Czaja, A., 2004: Why is North Tropical Atlantic SST variability stronger
in boreal spring? *J. Climate*, **17**, 3017-3025

Hasselmann, K., 1976: Stochastic climate models. I, Theory. *Tellus*,
**28**, 473---485.

Hastenrath, S., and L. Heller, 1977: Dynamics of climate hazards in
Northeast Brazil, *Q. J. R. Meteorol. Soc.*, **103**, 77-92

Hastenrath, S., 1991: *Climate Dynamics of the Tropics*. Kluwer Academic
Publishers, 488pp.

Okajima, H., S.-P. Xie and A. Numaguti, 2003: Interhemispheric coherence
of tropical climate variability: Effect of climatological ITCZ. *J.
Meteor. Soc. Japan*, **81**, 1371-1386.

Pierce, D.W., T. P. Barnett and M. Latif, 2000: Connections between the
Pacific Ocean tropics and midlatitudes on decadal time scales. *J.
Climate*, **13**(6), 1173--1194.

Kalnay, E., et al., 1996: The NCEP/NCAR 40-Year Reanalysis Project.
*Bull. Amer. Meteor. Soc.* **77,** 437-471

Lau, N.-C., 1997: Interactions between global SST anomalies and the
midlatitude atmospheric circulation. *Bull. Amer. Meteor. Soc.*
**78**(1), 21-33

Linkin, M. E. and S. Nigam, 2007: The North Pacific Oscillation / West
Pacific teleconnection pattern: Mature phase structure. *J. Climate*,
*Submitted*.

Ramage, C. S., 1995: Forecasters guide to tropical meteorology. AWS TR
240 updated, 489 pp. \[Air Weather Service Scott AFB, IL.\]

Rogers, J. C., 1981: The North Pacific Oscillation. *J. Climatol.*,
**1**, 39-57

Trenberth, K. E., G.W. Branstator, D. Karoly, A. Kumar, N.-C. Lau and C.
Ropelewski, 1998: Progress during TOGA in understanding and modelling
global teleconnections associated with tropical sea surface
temperatures. *J. Geophys. Res.*, **103**, 14291--14324.

Vimont, D.J., D.S. Battisti, and A.C. Hirst, 2001: Footprinting: A
seasonal connection between the tropics and mid-latitudes. *Geophys.
Res. Lett*., **28**, 3923--3926

Vimont, D.J., D.S. Battisti, and A.C. Hirst, 2003a: The seasonal
footprinting mechanism in the CSIRO general circulation models. *J.
Climate*., **16**, 2653--2667

Vimont, D.J., J.M. Wallace, and D.S. Battisti, 2003b: The seasonal
footprinting mechanism in the Pacific: Implications for ENSO. *J.
Climate*., **16**, 2668--2675

Vimont, D. J., 2005: "The contribution of the interannual ENSO cycle to
the spatial structure of decadal ENSO-like variability". *J. Climate*,
**18** (12), 2080-2092

Walker, G. T., and E. W. Bliss, 1932: World Weather V. *Mem. Roy.
Meteor. Soc.*, **4**, 53-84.

Xie, S.-P., and S. G. H. Philander (1994), A coupled ocean--atmosphere
model of relevance to the ITCZ in the eastern Pacific, *Tellus, 46A,*
340--350.

Xie, S.-P. (1997), Unstable transition of the tropical climate to an
equatorially asymmetric state in a coupled ocean-atmosphere model. *Mon.
Wea. Rev.,* **125,** 667--679.

Xie, S.-P., and Y. Tanimoto, 1998: A pan-Atlantic decadal oscillation.
*Geophys. Res. Lett.,* **25,** 2185--2188.

Xie, S.-P. (1999), A dynamic ocean--atmosphere model of the tropical
Atlantic decadal variability. *J. Clim.,* **12,** 64--70.

Zhang, Y., J. M. Wallace and D. S. Battisti, 1997: ENSOlike interdecadal
variability: 1900-93. *J. Climate*, **10**(5), 1004--1020.

**List of Figures**

FIG. 1. Wintertime SLP \[black contours; 0.5 mb (std dev)^-1^\] and net
surface heat flux \[white contours and shading; 5 W m^-2^ (std
dev)^-1^\] anomalies associated with the NPO. Results are presented for
(a) intra-seasonal time scales from the observed NCEP-reanalysis record,
and (b) an uncoupled CAM3 simulation. Solid contours denote positive
anomalies, dashed contours denote negative anomalies, and the zero
contour has been omitted. See text for a description of how the two maps
are derived. The imposed heat flux for the forced ensemble simulations
is twice the heat flux map in panel (b).

FIG. 2. Evolution of the model SST (shading and contours; 0.2C) and
lower level wind (vectors; scale at the bottom of the last panel)
response to the imposed surface heat flux. For SST, solid contours and
light shading denote positive anomalies, dashed contours and dark
shading denote negative anomalies, and the zero contour has been
omitted. Anomalies are defied as one half the difference in ensemble
means between the WARM and COLD simulations (see text for description of
model simulations). Lower level winds are shown only where the ensemble
mean is statistically significant at the 95% level, based on a
multivariate t-test.

FIG. 3. Heat budget averaged over the NWTP region (120E - 180, 0 - 15N).
Shown is the SST (thick solid line); net surface heat flux (thick dashed
line); latent heat flux (dashed line with 's); downward shortwave
radiation (dashed line with 's); and the sum of the net surface longwave
radiation, sensible heat flux, and imposed QFLUX (dotted line with 's).
Shading is used to emphasize the positive WES feedback over the region.
Also shown are the anomalous (thick vectors) and mean (thin vectors)
averaged lower level wind anomalies (vector origins are collocated with
the month that they represent). Note that for visualization, vector
*anomalies* are scaled by four times the scaling for the *mean* winds.
All anomalies are computed as one half the difference of the WARM and
COLD ensemble averages. By convention, downward heat flux anomalies are
positive.

FIG. 4. Heat flux components from the NPO-forced CAM3+SOM simulations
during boreal spring (AMJ, left column) and summer (JAS, right column).
Shown are: (a and b) net surface heat flux (contour 4 W m^-2^) and SST
(shaded where amplitude exceeds 0.2 C); (c and d) downward shortwave
radiation (contour 4 W m^-2^) and precipitation (shaded where amplitude
exceeds 0.4 mm day^-1^); (e and f) model latent heat flux (contour 4 W
m^-2^) and sum of bulk latent heat flux estimates from zonal wind,
meridional wind, and humidity anomalies (shaded where amplitude exceeds
4 W m^-2^); (g and h) bulk latent heat flux estimates from zonal wind
anomalies (contour 4 W m^-2^) and actual zonal wind anomalies (shaded
where amplitude exceeds 0.2 m s^-1^); (i and j) bulk latent heat flux
estimate from humidity anomalies (contour 4 W m^-2^) and SST anomalies
(shaded where amplitude exceeds 0.2 C). In all panels positive values
are denoted by solid contours (light shading), negative values are
denoted by dashed lines (dark shading), and the zero contour has been
omitted. By convention, downward heat flux anomalies are positive.

FIG. 5. Latent heat flux sensitivity to changes in the zonal component
of the lower-level wind (shaded) plotted with the mean wind (vectors)
from the CAM3+SOM control experiment. Data are shown for (a) AMJ, (b)
JAS, (c) OND, and (d) JFM. Positive values indicate that a westerly wind
anomaly will reduce evaporation, implying a downward anomalous latent
heat flux.

FIG. 6. Streamlines of lower level winds over the Pacific and Indian
Ocean, shown from the CAM3+SOM control simulation during (a) April and
(b) July.

FIG. 7. Lower level zonal wind (black contours, 0.5 m s^-1^) and net
surface heat flux (shaded with white contours, 10 W m^-2^) from the
uncoupled CAM3 simulations (SST anomalies are specified as shown in Fig.
8a). Solid lines denote positive values, dashed lines denote negative
values, and the zero contour has been omitted.

FIG. 8. Lower level zonal wind (contour 0.5 m s^-1^) and SST (shaded
with white contours, 0.2C ) during Jul-Sep from: (a) the uncoupled CAM3
simulation (SST is specified as shown); (b) months 1-3 of the JulIvp
experiment; and (d) months 4-6 of the AprIvp experiment. For comparison,
the difference between panels (b) and (a) is plotted in panel (c); and
the difference between panels (d) and (b) is plotted in panel (e). Solid
lines denote positive values, dashed lines denote negative values, and
the zero contour has been omitted.

TABLE 1. Summary of model simulations used throughout the study.

+--------------+-------------+-----------------+----------------------+
| # Name       | # Model     | **Forcing /     | # Notes              |
|              |             | Initial Value** |                      |
+--------------+-------------+-----------------+----------------------+
| CAM3 control | Uncoupled   | None            | Used to calculate    |
| (200 yr)     | CAM3 (no    |                 | imposed NPO forcing  |
|              | SST         |                 | and to generate      |
|              | evolution)  |                 | Q-flux. (Figure 1b)  |
+--------------+-------------+-----------------+----------------------+
| CAM3+SOM     | CAM3+SOM    | None            | 20yr continuous      |
| control      |             |                 | simulation. Last 10  |
|              |             |                 | years used for       |
|              |             |                 | analysis (Figures 4, |
|              |             |                 | 5, and 6) and        |
|              |             |                 | initial conditions   |
+--------------+-------------+-----------------+----------------------+
| NPO-WARM     | CAM3+SOM    | 2NPO heat flux  | Two ensemble         |
|              |             | imposed from    | simulations (10      |
| NPO-COLD     |             | Nov-Mar (Figure | members) initialized |
|              |             | 1b)             | in Nov, and run for  |
|              |             |                 | 18 months each.      |
|              |             |                 | (Figures 2, 3, 4)    |
+--------------+-------------+-----------------+----------------------+
| CAM3         | Uncoupled   | Idealized SST   | Two ten year         |
| SST-forced   | CAM3 (no    | (Figure 8a)     | simulations with     |
|              | SST         |                 | opposite polarity of |
|              | evolution)  |                 | SST forcing. (Figure |
|              |             |                 | 7).                  |
+--------------+-------------+-----------------+----------------------+
| IVP: (April, | CAM3+SOM    | Idealized heat  | Ensemble simulations |
| July,        |             | content anomaly | (both polarities of  |
| October,     |             | (corresponds    | the initial heat     |
| January      |             | identically to  | content anomaly; 10  |
| initialized) |             | the SST anomaly | members each)        |
|              |             | in Figure 8a)   | initialized in July  |
|              |             |                 | (JulIvp) and April   |
|              |             |                 | (AprIvp) and run for |
|              |             |                 | six months.          |
+--------------+-------------+-----------------+----------------------+

![](media/image5.jpeg){width="5.9944444444444445in"
height="2.089583333333333in"}

FIG. 1. Wintertime SLP \[black contours; 0.5 mb (std dev)^-1^\] and net
surface heat flux \[white contours and shading; 5 W m^-2^ (std
dev)^-1^\] anomalies associated with the NPO. Results are presented for
(a) intra-seasonal time scales from the observed NCEP-reanalysis record,
and (b) an uncoupled CAM3 simulation. Solid contours denote positive
anomalies, dashed contours denote negative anomalies, and the zero
contour has been omitted. See text for a description of how the two maps
are derived. The imposed heat flux for the forced ensemble simulations
is twice the heat flux map in panel (b).

![](media/image6.jpeg){width="3.1666666666666665in"
height="6.776388888888889in"}

FIG. 2. Evolution of the model SST (shading and contours; 0.2C) and
lower level wind (vectors; scale at the bottom of the last panel)
response to the imposed surface heat flux. For SST, solid contours and
light shading denote positive anomalies, dashed contours and dark
shading denote negative anomalies, and the zero contour has been
omitted. Anomalies are defied as one half the difference in ensemble
means between the WARM and COLD simulations (see text for description of
model simulations). Lower level winds are shown only where the ensemble
mean is statistically significant at the 95% level, based on a
multivariate t-test.

![](media/image7.jpeg){width="5.2756944444444445in"
height="3.0840277777777776in"}

FIG. 3. Heat budget averaged over the NWTP region (120E - 180, 0 - 15N).
Shown is the SST (thick solid line); net surface heat flux (thick dashed
line); latent heat flux (dashed line with 's); downward shortwave
radiation (dashed line with 's); and the sum of the net surface longwave
radiation, sensible heat flux, and imposed QFLUX (dotted line with 's).
Shading is used to emphasize the positive WES feedback over the region.
Also shown are the anomalous (thick vectors) and mean (thin vectors)
averaged lower level wind anomalies (vector origins are collocated with
the month that they represent). Note that for visualization, vector
*anomalies* are scaled by four times the scaling for the *mean* winds.
All anomalies are computed as one half the difference of the WARM and
COLD ensemble averages. By convention, downward heat flux anomalies are
positive.

![](media/image8.jpeg){width="5.9944444444444445in"
height="6.670833333333333in"}

FIG. 4. Heat flux components from the NPO-forced CAM3+SOM simulations
during boreal spring (AMJ, left column) and summer (JAS, right column).
Shown are: (a and b) net surface heat flux (contour 4 W m^-2^) and SST
(shaded where amplitude exceeds 0.2 C); (c and d) downward shortwave
radiation (contour 4 W m^-2^) and precipitation (shaded where amplitude
exceeds 0.4 mm day^-1^); (e and f) model latent heat flux (contour 4 W
m^-2^) and sum of bulk latent heat flux estimates from zonal wind,
meridional wind, and humidity anomalies (shaded where amplitude exceeds
4 W m^-2^); (g and h) bulk latent heat flux estimates from zonal wind
anomalies (contour 4 W m^-2^) and actual zonal wind anomalies (shaded
where amplitude exceeds 0.2 m s^-1^); (i and j) bulk latent heat flux
estimate from humidity anomalies (contour 4 W m^-2^) and SST anomalies
(shaded where amplitude exceeds 0.2 C). In all panels positive values
are denoted by solid contours (light shading), negative values are
denoted by dashed lines (dark shading), and the zero contour has been
omitted. By convention, downward heat flux anomalies are positive.

![](media/image9.jpeg){width="3.140277777777778in"
height="5.346527777777778in"}

FIG. 5. Latent heat flux sensitivity to changes in the zonal component
of the lower-level wind (shaded) plotted with the mean wind (vectors)
from the CAM3+SOM control experiment. Data are shown for (a) AMJ, (b)
JAS, (c) OND, and (d) JFM. Light (dark) shading indicates a sensitivity
that exceeds 15 (20) W s m^-3^.

![](media/image10.jpeg){width="3.232638888888889in"
height="2.5006944444444446in"}

FIG. 6. Streamlines of lower level winds over the Pacific and Indian
Ocean, shown from the CAM3+SOM control simulation during (a) April and
(b) July.

![](media/image11.jpeg){width="3.095833333333333in"
height="6.5680555555555555in"}

FIG. 7. Lower level zonal wind (black contours, 0.25 m s^-1^) and net
surface heat flux (shaded with white contours, 10 W m^-2^) from the
uncoupled CAM3 simulations (SST anomalies are specified as shown in Fig.
8a). Solid lines denote positive values, dashed lines denote negative
values, and the zero contour has been omitted.

![](media/image12.jpeg){width="5.9944444444444445in"
height="4.822916666666667in"}

FIG. 8. Lower level zonal wind (contour 0.25 m s^-1^) and SST (shaded
with white contours, 0.2C ) during Jul-Sep from: (a) the uncoupled CAM3
simulation (SST is specified as shown); (b) months 1-3 of the JulIvp
experiment; and (d) months 4-6 of the AprIvp experiment. For comparison,
the difference between panels (b) and (a) is plotted in panel (c); and
the difference between panels (d) and (b) is plotted in panel (e). Solid
lines denote positive values, dashed lines denote negative values, and
the zero contour has been omitted.