**Observations of Number range CHAPTERCloud, Radiation, and Surface
Forcing in the Equatorial Eastern Pacific**

[C.W. Fairall, Taneil Uttal, Duane Hazen]{.smallcaps}

*NOAA Earth System Research Laboratory*

*Boulder, Colorado*

[Jeffrey Hare]{.smallcaps}

*Cooperative Institute for Research in Environmental Sciences*

*University of Colorado and NOAA Environmental Technology Laboratory*

*Boulder, Colorado*

[Meghan F. Cronin]{.smallcaps}

*NOAA Pacific Marine Environmental Laboratory*

*Seattle, Washington*

[Nicholas Bond]{.smallcaps}

*Joint Institute for the Study of the Atmosphere and Oceans*

*University of Washington and NOAA Pacific Marine Environmental
Laboratory*

*Seattle, Washington*

[Dana E. Veron]{.smallcaps}

*College of Marine and Earth Studies, University of Delaware*

*Newark, Delaware*

October 30, 2006

Corresponding author address: C.W. Fairall, Physical Science Division,
NOAA ESRL, 325 Broadway, Boulder, CO 80305, chris.fairall@noaa.gov

ABSTRACT

In this paper we report on a study of cloud and surface flux processes
in the tropical eastern Pacific Ocean based on a series of ship-based
cloud and flux measurements program made during fall (1999--2002) and
spring (2000--2002) maintenance cruises along the 95W and 110W TAO buoy
lines. The year-to-year and seasonal variabilities of many of the
meteorological and oceanic means are relatively small. However, we find
notable seasonal variability in the northern branch of the
Inter-Tropical Convergence Zone, the north-south sea surface temperature
gradient, and heat fluxes north of the equator. In the fall, the
strengthening of the north-south SST contrast enhances convective
activity (more and deeper clouds, precipitation, southerly inflow) in
the area around 6N, 95W; diurnal variations of low cloud fraction were
weak. Spring clouds varied significantly over the diurnal cycle with
substantially lower cloud fraction during the day south of 5N.
Relatively low average cloud base heights around the equator are due to
chilling of the marine boundary layer over the cold tongue.

Cloud radiative forcing strongly correlates with cloud fraction; clouds
in the eastern equatorial Pacific cool the surface by about 40 Wm^-2^ in
both seasons. Cloud forcing estimates from the ship data, the TAO buoys,
and International Satellite Cloud Climatology Project (ISCCP) products
were combined to form a consensus observation dataset which is compared
with the NCEP Reanalysis-2 (NCEP2) and 40-yr ECMWF Re-Analysis (ERA-40)
cloud forcing values. The Reanalysis products were within 10 Wm^-2^ of
the observations for IR cloud forcing but substantially overestimated
the solar cloud forcing, particularly in spring.

**1. Introduction**

It is generally understood that a substantial part of atmospheric
predictability on seasonal and longer time scales is linked to the
predictability of the oceans and, in particular, sea surface
temperatures (SSTs) and that the tropics are the key region. Coupled
modeling of the mean state and the variability of the climate is prone
to biases and the tropical oceans are the most troublesome region
(Gleckler et al., 2006). Eastern Pacific examples of modeling challenges
include the cold bias and westward shift of SST in the equatorial cold
tongue, the \"double intertropical convergence zone (ITCZ)\" with too
much precipitation in the southern ITCZ, the misrepresentation of El
Nino/Southern Oscillation (ENSO) variability, and persistent problems in
the South American coastal upwelling zone and its associated
stratocumulus clouds. These biases are partly the result of poorly
resolved and poorly parameterized physics in coupled models (Ma et al.
1996; Philander et al. 1996). The importance of the turbulent fluxes can
be profoundly illustrated in the investigation of Miller et al. (1992),
where a small change in the bulk flux parameterization in the European
Center for Medium-Range Weather Forecasts (ECMWF) model completely
changed the modeled climate of the tropics. Recent studies with the
Community Climate Model (CCM3) have shown the clear need for
improvements in the representation of turbulent (Collins et al. 1997)
and radiative fluxes (Wild et al. 1995; Garratt and Prata 1996). Of
course, the variability of radiative fluxes at the surface is dominated
by clouds, and a major source of uncertainty in General Circulation
Models (GCM) is the representation of clouds and their interactions with
the surface radiative field (Browning 1994;Cess et al. 1995; Menon et
al. 2002; Siebesma et al., 2004).

The tropical eastern Pacific is a crucial component of the global
climate system, especially as related to its variability in association
with ENSO. Coupled model studies (Philander et al. 1996; Li and
Philander 1996; Delecluse et al. 1998; Phillips et al. 2006) indicate
that SST distributions in this region are sensitive to the radiative
effects of MBL clouds. This includes the classical subtropical
stratocumulus regimes off the coasts of California and Peru and the
equatorial stratocumulus occurring in the region of strong north-south
sea surface temperature (SST) gradient around latitudes 85--110W. Except
for some notable exceptions (Bond et al. 1992; Yuter et al. 2000;
Hashizume et al. 2003, deSzoeke et al. 2005), direct observations of
air-sea interaction processes in this region are rare. For example the
recent cloud GCM study by Siebesma et al. (2004) relied principally on
satellite data products because 'no reliable observations of fluxes are
available'.

Air-sea fluxes, clouds, and other MBL processes that influence flux
variability are a major focus of the US Climate Variations (CLIVAR)
program (Weller 1998; Esbensen et al. 2002; Raymond et al. 2003;
Bretherton et al. 2003) Pan American Climate Studies (PACS) and the
Eastern Pacific Investigation of Climate Processes in the Coupled
Ocean-Atmosphere System (EPIC). As one aspect of this effort, Tropical
Ocean Atmosphere (TAO) buoy observations were supplemented with
additional sites ands suites of sensors, as well as an enhanced
monitoring program of high-quality flux, cloud, and profile measurements
(Cronin et al. 2002, 2006a, 2006b) from the twice-yearly research vessel
cruises that service the 95 and 110W buoy lines (see Figure 1).

In this paper we present an analysis of the first four years (1999-2002)
of measurements from the ship-based component of the enhanced monitoring
program which was jointly implemented by the NOAA Earth Science Research
Laboratory (ESRL) and the NOAA Pacific Marine Environmental Laboratory
(PMEL). This paper complements two recent papers featuring analysis of
the TAO buoy observations along 95 and 110W: studies of the annual cycle
of cloud radiative forcing at the surface (Cronin et al. 2006a) and the
annual cycle of sensible and latent heat fluxes (Cronin et al. 2006b).
The first paper found disagreements as large as 100 Wm^-2^ between buoy
observations and NWP Reanalysis values; the second paper found
disagreements of the same order for latent heat flux. The disagreements
tended to be largest in spring and fall which conveniently corresponds
to the ship-based observations. The buoy data contain no direct
observations of clouds although some information can be obtained
indirectly via the cloud forcing phase diagram (see section 5). The ship
data include lidar ceilometer, microwave radiometer, and radar
observations of clouds which provide an additional basis for analysis of
cloud forcing.

The remainder of this paper is organized as follows. Details on the
ship-based flux and cloud property measurements are given in section 2.
The emphasis of the present analysis is on cross-equatorial variability
of the mean surface fluxes, the near-surface meteorology, and simple
cloud properties (section 3), contrasting the spring and fall seasons.
Following a discussion of cloud radiative forcing (section 4), we
present a similar cross-equatorial analysis of mean cloud properties and
cloud forcing (section 5) and compare in situ cloud forcing with
satellite and NWP products (section 6). Conclusions are given in section
7.

**2. The PACS/EPIC enhanced monitoring cruises**

During the twice-yearly maintenance cruises along the 95W and 110W TAO
buoy lines, we implemented a ship-based cloud and flux measurement
program to obtain statistics on a critical set of surface, MBL, and
low-cloud macrophysical, microphysical, and radiative properties.
Deployments on the NOAA Ships *Ka'imimoana* and *Ronald H. Brown* serve
to enhance the monitoring measurements from the TAO buoys. These
comprehensive data are expected to be useful for coupled
ocean-atmosphere modeling efforts, for MBL and cloud modeling, and to
improve satellite retrieval methods for deducing MBL and cloud
properties on larger spatial and temporal scales.

The southern end of the 95W line is in the subtropical stratocumulus
belt off the west coast of South America, and the 110W line is at the
western edge of the PACS region. The northern and southern branches of
the Inter-Tropical Convergence Zones (ITCZ) cross the TAO buoy lines at
approximately 8-10N and 5-8S latitudes, respectively. During the
Northern Hemisphere spring (from hereon, reference to seasons will be
for the Northern Hemisphere), the southern branch of the ITCZ is much
weaker than the northern arm and is essentially nonexistent in the fall.
For example, examination of the rainfall displayed in Figure 2 reveals
the branched structure of the ITCZ in the spring and also shows the
distinct change in rainfall climatology between the seasons. The
equatorial cold tongue is well developed at both longitudes, and
persistent low stratocumulus and occasional fog are encountered in this
area.

*a. Methodology*

Two portable flux measurement packages have been developed that can be
installed on either ship. The systems include instruments capable of
making observations of air-sea turbulent and radiative fluxes, along
with ceilometer measurements of cloud base, and rawinsonde profiles of
wind, temperature, and humidity. All cruises in this analysis included
the ESRL flux system and a basic cloud monitoring package, GPS
rawinsondes, a cloud ceilometer, and an automated two-channel microwave
radiometer (White et al. 1995; Fairall et al. 1997). On two cruises we
also fielded the NOAA Portable Cloud Observatory (NPCO), which features
the 35-GHz cloud radar (Moran et al. 1998), a two-channel microwave
radiometer (Snider and Hazen 1998), and an upward- looking IR
radiometer. A list of the instruments is shown in Table 1, where items
11--13 constitute the NPCO. More information on measurement systems and
accuracies can be found in Fairall et al. (1997) and Hare et al. (2005).

We completed seven measurement missions between fall 1999 and fall 2003.
The fall 2001 period included the regular TAO-PACS measurement
deployment plus the EPIC2001 field program (Raymond et al. 2004). Each
mission includes transects of the 95W and 110W buoy lines between 8S and
12N, along with transit legs to and from the experimental area.

*b. Database*

For the analysis presented in this paper, data time series from the flux
system, cloud ceilometer, and microwave radiometer are processed to
10-minute time resolution and integrated to a common time format. The
flux database includes turbulent and radiative fluxes and all relevant
near-surface bulk meteorological variables. The ceilometer cloud base
height information is processed to 10-min cloud statistics including
vertical cloud fraction and 15%, 50% (median), and 85% heights for the
cloud base cumulative distribution. The microwave radiometer data
include column integrated water vapor (*IV*) and integrated cloud liquid
water content (*W*). We have created a web-accessible database for our
PACS collaborators and other EPIC investigators. Data are archived for
public use at
[[ftp://ftp.etl.noaa.gov/user/cfairall/EPIC/epicmonitor/]{.underline}](ftp://ftp.etl.noaa.gov/et7/users/cfairall/EPIC/epicmonitor/).

**3. Cross-equatorial and seasonal dependence of ocean forcing and cloud
properties**

In this section, we examine the processed datasets from the first seven
cruises in the PACS region. By definition, a "climatology/monitoring"
project implies multiple measurements in the same region to evaluate
variability. In this region, the long term environmental variability is
dominated by El Niño/La Niña cycles, but in three years of
investigations reported here, it is possible to observe only a portion
of one cycle. There are significant seasonal differences between our
spring and fall cruises. There is also some difference between the 110W
and 95W transects. Finally, there is short-term variability associated
with the Madden-Julian oscillation, synoptic-scale fluctuations such as
easterly waves, and the Tropical Instability Waves (TIW) in the ocean
that cause latitudinal displacements of the SST fronts. An example of
the SST and wind fields obtained from the satellite observations for
both seasons is shown in Figure 1. In the remainder of this section we
will show various quantities of interest computed as one-day averages
and averaged further in latitude bins. One-day averages are chosen as a
convenience that removes the diurnal cycle and reduces much of the
sampling variability. There are some differences in the average
properties at 95W and 110W, but the seasonal variations dominate. As a
result, we have combined 95W and 110W data in the following
presentation.

*a. Meteorological variables and fluxes*

In Figure 3a, we show an example of the wind components for each cruise
to illustrate the variability at daily time scales. Figure 3b shows the
same data averaged by season: strong southeasterly winds occur south of
the equator with a minimum in the easterly component well north of the
equator and a clear strengthening of the northerly component approaching
the ITCZ in the fall. Wind speed is a maximum (about 7 ms^-1^) at the
southern end of the study area with a tendency for the spring winds to
be stronger.

We have computed the standard deviations of the ensemble daily averages
of the environmental variables within each latitude/seasonal bin. The
results show small latitudinal variations, which we have summarized in
Table 2. The sampling uncertainty at a given bin/season is the standard
deviation divided by the square root number of samples (typically
5--10). Thus, the uncertainties in the individual *mean* values shown in
Figure. 3b and the related plots to follow are the standard deviations
given in Table 2 multiplied by approximately one-third.

The seasonal SST cycle is strongest (about 6 C) at the equator and
further south, and becomes negligible to the north at about 10N. The
strong seasonal variation in SST is not mirrored in the
latitudinally-averaged sea-air temperature difference, which is an
indication of the boundary layer adjustment processes. There is a
slightly positive minimum in sea-air temperature difference
(*T~s~-T~a~*) at or just south of the equator. The maximum in
*T~s~*-*T~a~* (about 2.0 C in the fall) occurs at 5N, which is well
downwind of the prevalent SST front around 2--3N. A relatively strong
residual *T~s~-T~a~* exists in the fall, which indicates that the
surface forcing is greater in the fall, and that the MBL response to the
surface forcing is not rapid enough to reduce the sea-air temperature
difference such as is seen in the spring. Because *T~s~-T~a~* is the
result of a dynamic balance between surface fluxes, entrainment, and
radiative fluxes, it is difficult to explain this response
quantitatively without a detailed model study. The boundary layer
moisture pattern (as defined by specific humidity and column water
vapor) is closely coupled to SST. Total column water vapor actually
shows a much stronger seasonal variation (i.e., a factor of 2 versus
about 30% for MBL moisture). The upper air is much drier south of about
2N during the fall, presumably because of the overpowering influence of
deep convection in the northern ITCZ. This has important implications
for cloud top IR radiative cooling.

The latitudinal means of surface sensible and latent heat fluxes are
shown in Figure 4. The turbulent fluxes have minima at, or just south
of, the equator, with significantly lower sensible heat fluxes occurring
during the spring. Negative sensible heat fluxes are observed
episodically, but the overall average is slightly positive. The strong
MBL response north of the equator in the fall is the combined result of
the greater sea-air temperature difference, accelerating winds, and the
large change in hydrostatic stability. A similar latitudinal signal in
the fluxes is reported by deSzoeke et al. (@005) based on EPIC data from
fall 2001. Solar flux (Figure 5) is stronger north of the equator in the
spring (approaching summer) and south of the equator in the fall
(approaching winter). The normal latitudinal solar zenith angle
variation is somewhat masked by the relatively large cloud forcing
effects (see section 6). The northern ITCZ is readily apparent as
decreased net solar and increased net IR flux around 5--8N during the
fall season. The precipitation is most likely undersampled due to the
effects of ship flow distortion on the in situ sensor catchment (Yuter
and Parker 2001), but the strong northern ITCZ in fall (north of 6N) and
the double ITCZ structure in the spring (at approximately 4N and 4S) are
apparent. The peak values correspond roughly to the TRMM maps in Figure
2. However, the TRMM fall map puts the precipitation maximum around 8N,
which corresponds more closely with the radiative flux than the
precipitation measured on the ship.

*b. Cloud properties*

The low cloud fraction (Figure 6) data show the large convective cloud
masses in the fall associated with the most active phase of the ITCZ,
and the location of the peak corresponds well with the radiative results
in Figure 5. The southern ITCZ in spring is also clearly seen at 5S, but
there seems to be little evidence near 8S of the expected fall increase
in cloudiness associated with the edge of the Peruvian stratocumulus
region (Norris and Leovy 1994). As displayed in Figure 6, the 12-hr
(daytime) and 24-hr cloud fraction are plotted separately as a simple
indication of regions with diurnal variation in cloud fraction.
Stratocumulus MBL clouds tend to decrease in the local afternoon in
response to solar heating, while trade cumulus clouds tend to increase
in response to strong surface evaporation and instability. The results
in Figure 6 are somewhat unanticipated, with only the spring data away
from the southern branch of the ITCZ showing significant daytime
suppression of marine clouds. This suggests that in the spring the
clouds outside the ITCZ are more stratocumulus-like or that the
subsidence in those regions has a strong diurnal cycle with an afternoon
maximum.

Fair weather broken cumulus clouds are nearly ubiquitous in the
undisturbed tropical MBL, and they appear to be part of a
self-regulating system that produces low cloud base heights around 600 m
with astoundingly little variation. However, averaging cloud base
heights can disguise this, as when tilted trade cumulus clouds are
present in the field. In this case, the ceilometer may return
high-biased values for "cloud base" which are actually the tilted sides
of the clouds. Also, in the presence of deep convection there are often
outflow clouds at intermediate heights. Thus, a simple average of
ceilometer readings tends to be biased toward higher values than the
actual MBL cloud bases. Our initial approach to improve the cloud base
height characterization was based on the cumulative height probability
distribution of the samples in a 10-min time window. The 50% point of
the cumulative distributions is the median cloud height (i.e., 50% of
the observations have lower cloud base height and 30&=% have higher).
Similarly, the 15% point of the distribution defines the height where
15% of the observed cloud heights are lower and 75% are higher. We find
that the 15% distribution height tends to reduce the influence of tilted
clouds and gives a better estimate of the local BL cloud base height.
These data suggest seasonal differences only in the northern region.
Another approach is to examine the probability distribution of base
heights and look for the peak. In this case, we find a peak at 600 m for
most latitudes during both spring and fall seasons. There is typically a
secondary peak between 1000--1500 m caused by convective cloud top
horizontal spreading at the height of the inversion. Exceptions to the
common 600 m warm convective cloud base arise for cloud base heights at
about 500--550 m in the core of the ITCZ and when double-peaked
distributions (450 and 600 m) occur within 2 degrees of the equator. The
additional lower bases near the equator are caused by increased
stratification associated with the atmospheric flow over the cold
tongue. In the case of the equator during fall, the cloud base height
distribution has no dominant peak, but instead is essentially flat from
100 to 800 m. The very low-based clouds are consistent with surface
cooling or "advection fog", while the relatively high-based clouds
appear to often originate south of the equator, and be associated with a
decoupled boundary layer.

Measurements of time-averaged cloud liquid water path are shown in Table
3. Dividing these values by cloud fraction (Figure 6) gives us the mean
*W* when clouds are present, which provides a clearer representation
similar to the "cloudy pixel liquid water" in satellite *W* retrievals.
The higher *W* values (300 gm^-2^) associated with the northern ITCZ are
similar to those given by Weng et al. (1997). South of 3N we see the
much lower *W* values (20--120 gm^-2^) for stratocumulus/tradecumulus
clouds. The average for fall is 45 gm^-2^, and for spring it is 70
gm^-2^.

**4. Cloud radiative forcing**

Cloud forcing (CF) provides an indication of the impact of clouds on the
energy budget, and this analysis demonstrates the particular emphasis of
our observations in the PACS/EPIC region. Cloud forcing is the
difference in the observed mean radiative flux versus what the flux
would be in the absence of clouds:

$\text{CF}_{x}\text{=<}R_{x} > - R_{x0} >$, **(1)**

where *R* is the radiative flux, the subscript *x = s* for solar or *x*
= *l* for longwave (IR), and the subscript 0 refers to the clear-sky
flux. The enhanced monitoring provided direct measurement of clearsky
radiation. From these measurements, clear-sky flux algorithms were
tested and tuned for the seasonal variations observed in these regions
(see Appendix A). A related variable that is often used is the *maximum*
cloud forcing, which is the conditional change in the flux when a cloud
is actually present:

$\text{MCF}_{x}\  = \  < R_{x1} > - R_{x0} > \  \approx \frac{\ \text{CF}_{x}}{f}$
, **(2)**

where *R~x~*~1~ is the radiative flux for overcast conditions and *f* is
cloud fraction. CF averages clear and cloudy periods, but MCF is the
difference between overcast (cloud fraction, *f* = 1.0) and clear (*f* =
0) conditions. MCF is related to the radiative properties of individual
clouds and can, in principle, be directly computed from microphysical
and radiative variables, while CF is strongly dependent on whether it is
cloudy or not. MCF also is applied in simple models that use cloud
fraction to estimate cloud effects on radiative fluxes (see section 5).
For this paper, we will use the CF of the *net* surface flux (SCF),
which is the quantity most relevant to the surface heat balance of the
ocean. Net SCF is downward flux multiplied by 1- α (where α is albedo,
and is estimated at 0.945 for solar wavelengths over the sea) or
multiplied by the broadband IR emissivity (ε) of seawater (estimated as
0.97; Fairall et al. 1996a).

CF has seen extensive application as an index of the importance of
clouds for the global heat balance (e.g., Ramanathan et al. 1995 for the
Tropics; Walsh and Chapman 1998 for the Arctic). This variable yields
valuable information about cloud dynamics (Pincus et al. 1997) and is an
important tool for diagnosing GCM treatments of cloud/radiative
processes. Ramanathan et al. (1995) showed a direct link between surface
cloud forcing and oceanic dynamics. Furthermore, Tian and Ramanathan
(2002) have shown that CF is much more directly linked to atmospheric
regional dynamics and moisture transports than surface turbulent fluxes.

Cloud forcing can be inferred globally using satellite data at the top
of the atmosphere (TOA, Ramanathan et al. 1989), and it can be
determined more indirectly from surface data. For SCF, the surface-based
methods are more direct and more accurate, but provide limited sampling.
Previous studies in the tropics have shown that the heavy water vapor
burden in the boundary layer partially masks the longwave (LW) signal
from clouds, and SCF is dominated by the solar flux, as LW SCF is about
5 to15 Wm^-2^ while shortwave (SW) SCF ranges from -50 to -90 Wm^-2^
(Stephens and Webster 1981). In subtropical stratocumulus regimes, the
solar component is more nearly balanced by the LW component and the
total SCF is perhaps closer to -30 Wm^-2^.

This SW versus LW compensating effect depends strongly on latitude and
season. Tropical regions have the strongest solar cloud forcing, while
polar areas tend to have the strongest relative IR cloud forcing. In the
upper midlatitude storm track, total SCF tends to be small because the
solar and IR components approximately cancel.

**5. Cloud forcing analysis**

In this section we will examine two aspects of SCF in the eastern
equatorial Pacific: the seasonality of SCF (similar to the other
atmospheric properties presented in section 3) and the mean dependence
of SCF on cloud fraction.

The latitudinal dependence of clear-sky net fluxes is shown in Figure 7.
The counterintuitive asymmetry about the equator for the clear-sky solar
flux is caused by the time of year of the observations (see comment
about seasonal variations in column water vapor in section 4). The more
negative clear-sky net IR flux in the fall is caused by the lower
atmospheric water vapor during that season. The slightly stronger
latitudinal variation of net IR flux in the fall is associated with
stronger variations in atmospheric temperature and water vapor. The
average of the two seasonal curves gives a net solar clear-sky flux of
about 300 Wm^-2^ and a net IR clear-sky flux of -68 Wm^-2^ north and -78
Wm^-2^ south of the equator. Based on Earth Radiation Budget Experiment
(ERBE) data, Tian and Ramanathan (2002) report annual averages for solar
flux in the range 270--275 Wm^-2^ and IR flux of about -60 Wm^-2^ in the
north and -80 Wm^-2^ in the south. Our results are not annual averages,
so the significance of the similarities is limited without further
study.

The mean SCF for both solar and IR is shown in Figure 8 for both SW and
LW fluxes; the most obvious seasonal SCF difference is the larger
magnitudes associated with greater cloudiness in the northern ITCZ.
Using the cloud fraction from Figure 6, we have computed the maximum
surface cloud forcing (MSCF), and this is illustrated in Figure 9. Here
we used the 24-hr cloud fraction for IR flux and the clear-sky solar
flux-weighted cloud fraction for solar flux. The 24-hr cloud fraction
does not correlate well with daily SW SCF and, in the case of the mean
day-night differences in *f*, may lead to biases in solar MSFC. Because
the daily average solar flux is relatively sensitive to cloudiness
around local noon, we find that the clear-sky solar-weighted cloud
fraction provides a better representation than the daytime *f*. For all
latitudes, the IR MSCF is about 15 Wm^-2^ lower in spring than in fall.
The daily values plotted on the SCF phase diagram (Figure 10) provide a
striking contrast to the typical behavior in most of ESRL's 11 other
tropical cruises (Fairall et al. 2003; Hare et al. 2005). Rather than
tending to lie near a single characteristic line, the PACS/EPIC data are
scattered from tropical to storm track lines. This clearly indicates
that all three principal climate zones (tropical, trade wind, and
stratocumulus) are encountered within 10 degrees of the equator in the
eastern Pacific. From this figure, we interpret that the spring has a
more tropical signature while the fall period is biased toward trade
wind and stratocumulus behavior. The deep convective events in the fall
often have very strong cold-air downdrafts that lead to sharp reductions
in the near surface air temperature which might invalidate the
simplistic representation of the IR clear-sky flux.

Of course, cloud forcing depends on cloud fraction. In the simplest
case, each cloud's radiative properties are similar to all others, and
then CF depends upon the number of clouds in the field. Thus, the
radiative flux at the surface can be written

$R_{x}\  = \ R_{x_{0}}\  + \ f\  \ast \ \text{MCF}_{x}\  = \ (1\  - \ f)R_{x_{0}}\  + \ \text{fR}_{x_{1}}$.
**(7)**

This approach forms the basis for simple treatments of fractional
cloudiness (Hartmann et al. 1992). For solar flux, nonlinear
dependencies on vertical *f* are common (Katsaros 1990; Grant and
Hignett 1998). In addition, hemispherically-averaged cloud fraction or
cloud fraction at the solar zenith angle (White et al. 1996) have also
been used. Thus, a linear dependence of CF on cloud fraction implies
that such simple models are applicable. Here, we illustrate this
dependence using the daily-averaged solar and IR fluxes with a 24-hr
averaged (for IR) or daytime averaged (for solar) cloud fraction (Figure
11). For IR flux, a linear regression gives MSFC~l~ = 50 Wm^-2^.
However, a 2^nd^-order fit gives a linear term of 33 Wm^-2^ and a
quadratic coefficient of 20 Wm^-2^. Thus, for low cloud fractions IR
MSFC is less than for high cloud fractions (i.e., 33 vs. 53 Wm^-2^). For
solar flux, the data are too scattered to determine nonlinearity, but a
linear regression results in MSFC~s~ = -135 Wm^-2^.

Figure 12 shows the results of averaging the daily values into bins of
cloud fraction where we separate spring (74 days) and fall (86 days)
cruises. This figure clearly shows lower LW SFC in the spring than in
the fall. For cloud fraction less than 0.5, the fall SW SFC values are
smaller in magnitude, but slightly larger for high cloud fractions
(greater than 0.7). This is qualitatively consistent with the lower
cloudy liquid water path values found south of 3N and the larger values
north of 6N in fall versus spring (see the discussion at the end of
section 5). Therefore, for solar SCF there are some small seasonal
differences that are not attributable to cloud fraction but are probably
associated with the actual liquid water content of the clouds. Linear
regressions to these data imply IR MSFC of 53 Wm^-2^ in fall and 40
Wm^-2^ in spring, with solar MSFC values of -141 Wm^-2^ in fall and -127
Wm^-2^ in the spring. Averaging in this form has removed much of the
apparent nonlinearity from Figure 11, suggesting that it was partly
caused by mixing spring and fall data.

**6. Intercomparsions of *In Situ* Radiative Fluxes with NWP and
Satellite values.**

In this section we examine different sources of data for estimating
solar and IR cloud forcing of net radiation at the surface. Three
sources of data are used: ESRL direct measurements from the ship,
NOAA/PMEL direct measurements from the TAO buoys, and satellite
estimates from the International Satellite Cloud Climatology Project
(ISCCP). These have been compared to ERA-40 and NCEP2 reanalysis
products. Details on the TAO, ISCCP, and reanalysis products are given
in Cronin et al. (2005b). The ESRL radiative flux instruments are tended
daily and are considered to have the best absolute accuracy. However,
their temporal sampling is poor (a few days at each latitude bin each
transect). Averaging over multiple cruises reduces the sampling errors,
but the TAO buoys are still superior in this respect. Because the buoy
sensors are unattended for six month intervals, their absolute
calibration is more problematic. The accuracy of the buoy measurements
has been assessed by comparing with the ESRL measurements in Cronin et
al. (2006b). Paradoxically, the practicalities of replacing the buoys
results in very little direct intercomparison with ship-based sensors
while the ship is close to the buoy (proximity is critical for radiative
flux comparisons). The satellite data has good temporal sampling
(essentially continuous), but must be considered an indirect
measurement.

The result of a comparison of these data is shown in Figure 13. The
'measurements' are indicated with thin dotted lines and the two NWP
products with heavy dashed lines. A consensus estimate based on the
three measurement sets is indicated by the dark red line. The fall data
show a strong ITCZ at about 6 N; the spring has a weaker double ITCZ
signature. The buoy solar SFC tends to be about 20 Wm^-2^ more negative
than the ESRL and ISCCP data, but the significance is unknown so we have
treated the three sets as equivalent. The standard deviations of the
individual measurement sets (after averaging over latitude) from the
consensus is 3 Wm^-2^ for IR and 11 Wm^-2^ for solar SFC. Solar SFC is
about three times greater than IR, which is consistent with the larger
standard deviation.

With the possible exception of spring NCEP2, the NWP and observational
data are essentially indistinguishable, on average, for IR SFC. Neither
NWP product diagnoses the observed enhancement in IR SFC at the ITCZ,
with the exception of the ERA-40 for the ITCZ north of the equator in
spring. For solar SFC, both NWP products have errors exceeding 30
Wm^-2^. In spring the latitude-averaged bias is 32 Wm^-2^ for ERA-40 and
60 Wm^-2^ for NCEP-2 (i.e., the NWP products have too much cloud or
their clouds are too dark). ERA-40 overemphasizes the ITCZ; NCEP-2 gets
the latitudinal structure completely wrong. In fall the
latitude-averaged bias is 8 Wm^-2^ for ERA-40 and 45 Wm^-2^ for NCEP-2.
Here ERA-40 is better, but still gets too much SFC in the ITCZ. It is
puzzling that the latitudinal structures in the NWP products are so much
more prominent in the solar SFC than in the IR SFC. The implication is
that they are handling the deep clouds in the ITCZ considerably better
than the MBL clouds, since higher clouds don't affect the IR SFC.

**7. Conclusions**

We analyzed air-sea flux and cloud measurements from a series of seven
cruises made in the eastern equatorial Pacific between fall 1999 and
fall 2002. These cruises were transects along the enhanced TAO buoy
lines along 95W and 110W longitudes and afforded multiple sampling of
the equatorial cold tongue and ITCZ. Our analysis has emphasized the
statistical properties (mean, variance, probability distribution
functions) as a function of latitude contrasting the Northern Hemisphere
fall and spring seasons. Seven cruises do not constitute a comprehensive
climatology, of course, but many of the mean variables show consistent
structures. For most quantities, the variability from year to year
during the same season and at the same latitude is quite small (see
Table 2), and, with a few exceptions, there was little difference
between the two seasons.

The most obvious seasonal differences are the stronger Northern
Hemisphere ITCZ and stronger north-south SST difference in the fall
periods. We also see much stronger turbulent heat fluxes in the SST
frontal region north of the equator in the fall. These stronger fluxes
drive the rapid MBL adjustment to the changing SST associated with the
cross-equatorial air flow. Thus, the colder water south of the equator
in fall exerts a strong cooling influence on the ocean north of the
equator (an additional 60 Wm^-2^ compared to spring). The well-defined
ITCZ region north of the equator in the fall also produces much more
cloudiness, precipitation, southerly winds, and solar cloud forcing in
the region around 6N.

We examined cloud fraction, cloud base height statistics, and cloudy
liquid water path. The fall period was much cloudier north of the
equator, with mean cloud fraction about 90% at 6N. The spring clouds
showed a strong diurnal variation with substantially lower cloud
fraction during the day south of 5N (except in the southern ITCZ region
at 4S). This may be a result of greater subsidence or surface heating
during the day. Simple averages of cloud base heights were presented,
and, through consideration of the peak in the distribution (i.e., the
most probable cloud base height), we demonstrated the significant
effects on clouds near the equator. The lower cloud bases there reflect
cooling as air from south of the equator flows over the cold tongue,
thereby stabilizing the lower MBL and decoupling the upper MBL from the
surface. The equatorial effects were more dramatic in the fall when the
cold tongue is much colder. At the equator, very low clouds were
occasionally observed with a fairly even distribution of cloud bases.

Surface cloud radiative forcing is defined as the difference in mean
observed net radiative flux and that flux which would be observed in the
absence of clouds. To investigate this, we first estimated the clear-sky
radiative fluxes, and we employed two simple models requiring
near-surface temperature, humidity, and estimates of column water vapor
and aerosol optical depth. The model parameters were tuned for a best
fit to the radiative fluxes observed during clear periods. By turning
the models to the data, we are able to produce estimates of SFC within
5--7 Wm^-2^ for daily-averaged radiative fluxes. The models are intended
to be applicable for buoys and ships where column water vapor
measurements are not available, although regional accuracy will be
compromised without specific tuning.

For IR fluxes, SFC strongly correlates with cloud fraction. There are
substantial seasonal differences in IR SFC but relatively modest
seasonal differences in MSFC (53 Wm^-2^ in the fall versus 40 Wm^-2^ in
spring). For solar fluxes, MSFC is about -135 Wm^-2^ ; MSFC~s~ is
stronger in the spring between 3S and 6N with values around -250 Wm^-2^
at and just south of the equator (Figure 9). However, these unusual
spring values are actually realized in regions with low daytime cloud
fraction, so the significance is uncertain. When averaged in cloud
fraction bins, the fall versus spring differences are fairly small
(Figure 12). There is some evidence for lower liquid water leading to
lower solar MSFC for broken clouds in the fall, but these differences
are near the sampling resolution of the averages. The ratio of solar to
IR MSFC (-135/53 = -2.5) implies that clouds in this region strongly
cool the surface. However, the IR SFC is significantly greater than the
observations for a typical ESRL tropical cruise (see Figure 10), so the
net cooling effect is less than that of the tropical western Pacific.

SCF was examined further by comparing observations with NWP reanalysis.
The ESRL means were combined with buoy and satellite monthly averages of
SFC for fall and spring to obtain a consensus estimate based on
measurements. The individual data sources agreed within about 10 Wm^-2^
with the consensus values. The Reanalysis products were within 10Wm^-2^
of the observations for IR SCF but substantially overestimated the solar
SFC, particularly in spring. The inconsistent behavior of the Reanalysis
products (IR versus Solar) suggests the models are generating incorrect
distributions of cloud types (see Cronin et al. 2006a for further
discussion).

Finally, the contributions of clouds to the net surface heat budget are
shown in Figure 14. The net heat flux (upper panel) shows a striking
symmetry difference between the spring and fall. The spring net heat
flux is nearly symmetrical about the equator with a maximum (175 Wm^-2^)
at the equator decreasing to about 100 Wm^-2^ at 10N and 8S. The
equatorial maximum is associated with lower turbulent fluxes and
modestly lower cloudiness at the equator. In the fall, the maximum net
heat flux (180 Wm^-2^) is at 2S and the minimum (essentially 0) is at
6N. There is also a significant asymmetry away from the equator (125
Wm^-2^ at 8S versus 75 Wm^-2^ at 10N). Clearly, the minimum SFC at 6N is
primarily associated with strong solar SFC (lower panel in Figure 13)
associated with the ITCZ. The maximum just south of the equator is
associated with much weak turbulent cooling (Figure 4) and weak solar
SFC. Solar SFC is also much more equatorially asymmetric in the fall
(Figure 14).

We have described the statistical properties of the MBL and surface
forcing of the eastern equatorial Pacific ITCZ/Cold Tongue complex twice
a year at times near the maximum seasonal contrast. We have attempted to
not only characterize the variability but also to isolate the role of
cloud radiative forcing in that variability, and this includes providing
information on cloud properties. Because our measurements do not include
a strong El Niño event, this study cannot be considered a complete
characterization of the statistics, but does provide significant detail
to establish basic characterization for non-El Niño conditions.
Soundings from these cruises have already formed the basis of an
analysis of MBL evolution in the vicinity of the strong SST gradient in
fall (Pyatt et al. 2005) and a study of parameterizations of MBL depth
in climate models (Zeng et al. 2004). A much more complete investigation
of the annual cycle and inter-annual variability is underway using data
from the TAO buoys in this region (Cronin et al. 2002). In addition, an
analysis of C-band radar characterizations of deep convection is
presently under way. In 2003, we inaugurated a related study during an
annual fall cruise to the WHOI *Stratus* buoy at 20S 95W
(http://uop.whoi.edu/stratus/). This buoy is located in the heart of the
South Pacific stratocumulus belt off South America (Hartmann et al.
1992).

*Number range CHAPTERAcknowledgments*. This work was supported by the
NOAA Office of Global Programs, ONR Marine Meteorology program, the DOE
Atmospheric Radiation Measurement program, and the National Science
Foundation Climate Dynamics program. The authors especially thank Scott
Abbott, Dave Costa, Mike Falls, Dan Gottas, Jim Jordan, Jesse Leach,
Brenda Mulac, Jeff Otten, Bill Otto, Sergio Pezoa, Michelle Ratterree,
Cat Russell, Kara Sterling, and Dan Wolfe for work, dedication, and
personal sacrifice in doing the field work. The contributions and help
of the crews of the NOAA ships *Ronald H. Brown* and *Ka'imimoana* is
greatly appreciated.

Appendix A. **Clear-sky radiative flux parameterizations**

For the study of SCF, the realization of the fluxes at the air-surface
interface can be handled effectively by simple methods (e.g., Lind and
Katsaros 1984, Katsaros 1990; Key et al. 1996). For one thing, we only
require estimates of *clear-sky* flux because the all-sky flux is
directly measured. Furthermore, we can accommodate regional and seasonal
variability by tuning the model to fit the observations during clear
periods. For this approach, the challenge is to plainly identify the
clear periods. To do that, we use estimates of the vertical cloud
fraction from the ceilometer data. More detail on this process can be
found in Hare et al. (2005).

*a. IR flux*

Most IR parameterizations estimate the flux in terms of near-surface
parameters such as air temperature ,T~a~:

$R_{\text{ld}}\  = \ \varepsilon_{e}{\sigma T}_{a}^{4}$ , **(A1)**

where *ε~e~* is an effective total sky emissivity that, when combined
with the air temperature, yields an estimate of the IR flux. For
clear-sky conditions, *ε~e~* is an empirical function of atmospheric
water vapor content --- either local humidity, *q~a~*, or the total
integrated column water vapor, *IV*. Josey et al., 2003 offer a
variation using *T~a~* and dewpoint temperature depression, and Garratt
(2001) presents another utilizing *T~a~* and *IV* (see Table 2 for units
of these variables).

We have chosen the Brunt (1932) form for the parameterization of
effective emissivity for clear skies, *ε~e~*~0~:

$\varepsilon_{e0}\  = \ A\  + \ B\sqrt{q_{a}}$ . **(A2)**

Using the ESRL ship-based radiative flux observations from 15 campaigns
(Fairall et al. 2003), we have previously determined the A and B
coefficients as simple linear functions of latitude. This fit is based
on data obtained between the equator and 60N, but the majority of the
data were obtained within 15 degrees of the equator. For this study, we
optimized the fit using only data from the PACS cruises within 15
degrees of the equator. This gives a result:

$A\  = \ 0\text{.}\text{50}\  + \ \frac{0\text{.}\text{13}}{\text{60}}\text{abs}(\text{latitude})\ ;\ B\  = \ 0\text{.}\text{091}\  - \ \frac{0\text{.}\text{03}}{\text{60}}\text{abs}(\text{latitude})$.
**(A3)**

We also added *IV* for a three-parameter fit. The *IV* was obtained
through retrieval of surface-based microwave radiance. Adding the column
water vapor gives a fit of the form:

$\varepsilon_{e_{0}}\  = \ A\  + \ B\sqrt{q_{a}}\  - \ 0\text{.}\text{0188}\  + \ 0\text{.}\text{0063}\ \text{IV}$.
**(A4)**

Slight adjustments in the latitude dependences are required to provide a
better fit when using column water vapor in this expression. The fits
are based on approximately 2300 10-min observations (out of a total of
22,127 observations within 15 degrees of the equator with simultaneous
column water vapor measurements). Clear skies were diagnosed by
requiring zero cloud fraction from 25-min before to 25-min after the
observation time.

Figure A1 shows measurements and two-parameter model values of downward
IR flux for periods when the ceilometer-measured cloud fraction is 0 for
a continuous 60- min time period. Individual points well above the line
are caused by clouds in the field of view of the PIR but are not
directly above the ceilometer. We also computed the residuals between
the measured data and the model as a function of the column water vapor.
A linear fit of these residuals (with a few small adjustments to *A* and
*B*) was used to obtain the three-parameter model. The two-parameter fit
has a bias of -1.8 Wm^-2^ and a standard deviation of 7 Wm^-2^, whereas
the three-parameter fit has a bias of -0.9 Wm^-2^ and a standard
deviation of 5.7 Wm^-2^.

*b. Solar flux*

We are using the solar flux parameterization from Iqbal (1998) which
contains components to track the angular position of the sun as a
function of day of the year, time of day, and latitude. The solar flux
constant at the top of the atmosphere is set to 1367 Wm^-2^, and the
transmission coefficient of the atmosphere is estimated from
specifications of total column ozone, aerosol optical thickness in two
wave bands, and the column integrated water vapor *IV* as above. We then
tune the model by plotting the time series of measured *R~sd~*, and
comparing it to computed clear-sky values from the algorithm. The
aerosol values are then adjusted to fit the peak solar flux for clear
days on a cruise-by-cruise basis. We find that slightly different
aerosol coefficients are required in the fall versus the spring.

We have examined the accuracy of this method by computing the
probability distribution of the measured-modeled difference in solar
flux for cases where the solar zenith angle is less than 20 degrees and
cloud fraction is zero (471 samples meet these conditions). The peak of
this distribution occurs at 0 Wm^-2^ (i.e., no model bias), and it has a
half-width at half-maximum (an estimate of the standard deviation) of 20
Wm^-2^. Because some of this width is caused by cloud reflections and
random variability in the m\`easurements, we estimate the standard
deviation of the model near the solar peak to be 15 Wm^-2^ with a model
bias of about -5 Wm^-2^. For *daily average* solar fluxes where
measurements of column water vapor are available, these values
correspond to a model bias of -2 Wm^-2^ and a standard deviation of 5
Wm^-2^.

The bias and variability quoted for the clear-sky models are not
absolute accuracies, but are computed relative to the data from which
they are tuned. We are concerned with the mean measured flux minus the
mean clear-sky modeled flux. Because we have tuned the model to the data
we are analyzing, biases in the measurements will, to first order,
cancel in the computation of SCF. For daily average values, the
uncertainty in SCF will be about 5--7 Wm^-2^ for both IR and solar
fluxes.

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List of Tables

Table 1. Instruments deployed and measurements by ESRL for the
ship-based cloud/MBL monitoring project.

Table 2. Standard deviation of selected variables, computed for all
three years of cruise data during each season (spring or fall) within a
single latitude bin. The values presented are the average variance of
all latitude bins.

Table 3a. Mean ESRL observations along 95 and 110 W longitude for fall
1999-2002.

Table 3b. Mean ESRL observations along 95 and 110 W longitude for spring
2000-2002.

List of Figures

Figure 1. The EPIC mooring array is shown in relation to the September
1998 TRMM Microwave Imager SST and QuickSCAT wind fields. Diamonds
indicate the locations of TAO buoys.

Figure 2. One-month rainfall accumulations from the TRMM 3B42 rainfall
product for spring (a) and fall (b) 2000 cruises.

Figure 3. Wind components (northerly --- upper panel; easterly --- lower
panel) from seven EPIC cruises. (a) Daily-averaged values, with each
symbol representing a single cruise and includes both 110W and 95W
longitudinal portions. (b) Latitude average values. Circle --- fall;
diamond --- spring.

Figure 4. Latitude-averaged turbulent heat fluxes. Upper panel ---
sensible heat; lower panel --- latent heat. Circles --- fall; diamonds
--- spring.

Figure 5. Latitude-averaged net radiative heat fluxes. Upper panel ---
solar flux; lower panel --- IR flux. Circles --- fall; diamonds ---
spring.

Figure 6. Latitude-averaged low cloud fraction. Circles --- fall;
diamonds --- spring. Solid line --- 24-hr average cloud fraction; dashed
line --- daytime average cloud fraction.

Figure 7. Latitude-averaged net clear-sky radiative heat fluxes using
the models described in Appendix A. Upper panel --- solar flux; lower
panel --- IR flux; circles --- fall; diamonds --- spring. The strange
bump at 10N in the fall is caused by observations in the EPIC2001
project which occurred almost two months earlier than the typical fall
monitoring cruise.

Figure 8. Latitude-averaged radiative surface SFC. x --- fall IR flux;
closed circles --- spring IR flux; open circles --- fall solar flux;
diamonds --- spring solar flux.

Figure 9. Latitude-averaged radiative surface maximum SFC computed via
Eq. 2. x --- fall IR flux; closed circles --- spring IR flux; open
circles --- fall solar flux; diamonds --- spring solar flux.

Figure 10. Cloud forcing phase diagram showing the balance of solar and
IR SCF in different climate zones. An ensemble of individual days in a
particular region tend to lie along a line where values near zero from
small cloud fraction and values near the maximum are for cloud fraction
near 1.0. Symbols are for the data from this analysis: Circle --- fall;
x --- spring.

Figure 11. Daily-averaged SFC as a function of cloud fraction. Circle
--- IR; x --- solar. The solid lines represent nominal MSFC values of 50
Wm^-2^ for IR and -150 Wm^-2^ for solar fluxes.

Figure 12. As in Figure 11, but the daily data have been averaged in
bins of cloud fraction. Circles --- spring IR; plus --- fall IR;
diamonds --- spring solar; x --- fall solar.

Figure 13. Cloud forcing as a function of latitude averaged of the
spring and fall PACS cruises near the equator in the East Pacific: a)
Fall Solar SCF, b) Fall IR SCF, c) Spring Solar SCF, and d) Spring IR
SCF.. The symbols for measurements are: TAO buoys -- circle, ESRL ship
data -- square, ISCCP -- right pointing open triangle, mean of these
three -- heavy solid line. The symbols for the reanalysis products are:
NCEP2 -- left pointing triangle, and ERA40 -- star. ESRL data are
computed from cruise averages; buoy, satellite, and NWP are computed
from monthly averages in the cruise season window.

Figure 14. Latitude-averaged surface fluxes. Upper panel --- net heat
flux into the ocean; lower panel --- contribution to the net heat flux
accounted for by SCF. Circles ---fall; diamonds --- spring.

Figure A1. Comparison of clear-sky IR fluxes using model (x-axis) from
Eqs. 4--5 and measurements (y-axis) for 60-min cloud free periods.

+----+---------------------------------+-------------------------------+
| *  |                                 |                               |
| *T |                                 |                               |
| ab |                                 |                               |
| le |                                 |                               |
| 1. |                                 |                               |
| ** |                                 |                               |
| I  |                                 |                               |
| ns |                                 |                               |
| tr |                                 |                               |
| um |                                 |                               |
| en |                                 |                               |
| ts |                                 |                               |
| de |                                 |                               |
| pl |                                 |                               |
| oy |                                 |                               |
| ed |                                 |                               |
| a  |                                 |                               |
| nd |                                 |                               |
| me |                                 |                               |
| as |                                 |                               |
| ur |                                 |                               |
| em |                                 |                               |
| en |                                 |                               |
| ts |                                 |                               |
| by |                                 |                               |
| ES |                                 |                               |
| RL |                                 |                               |
| f  |                                 |                               |
| or |                                 |                               |
| t  |                                 |                               |
| he |                                 |                               |
| sh |                                 |                               |
| ip |                                 |                               |
| -b |                                 |                               |
| as |                                 |                               |
| ed |                                 |                               |
| c  |                                 |                               |
| lo |                                 |                               |
| ud |                                 |                               |
| /M |                                 |                               |
| BL |                                 |                               |
| mo |                                 |                               |
| ni |                                 |                               |
| to |                                 |                               |
| ri |                                 |                               |
| ng |                                 |                               |
| pr |                                 |                               |
| oj |                                 |                               |
| ec |                                 |                               |
| t. |                                 |                               |
+----+---------------------------------+-------------------------------+
| ** | **System**                      | **Measurement**               |
| It |                                 |                               |
| em |                                 |                               |
| ** |                                 |                               |
+----+---------------------------------+-------------------------------+
| 1  | Motion/navigation package       | Motion correction for         |
|    |                                 | turbulence                    |
+----+---------------------------------+-------------------------------+
| 2  | Sonic anemometer/thermometer    | Direct covariance turbulent   |
|    |                                 | fluxes                        |
+----+---------------------------------+-------------------------------+
| 3  | IR fast H~2~O/CO~2~ sensor      | Direct covariance             |
|    |                                 | moisture/CO~2~ fluxes         |
+----+---------------------------------+-------------------------------+
| 4  | Mean SST, air temperature/RH    | Bulk turbulent fluxes         |
+----+---------------------------------+-------------------------------+
| 5  | Pyranometer/Pyrgeometer         | Downward solar and IR         |
|    |                                 | radiative flux                |
+----+---------------------------------+-------------------------------+
| 6  | Ceilometer                      | Cloud base height             |
+----+---------------------------------+-------------------------------+
| 7  | 0.92 or 3 GHz Doppler radar     | Cloud top height, MBL         |
|    | profiler                        | microturbulence               |
+----+---------------------------------+-------------------------------+
| 8  | Rawinsonde                      | MBL wind, temperature,        |
|    |                                 | humidity profiles             |
+----+---------------------------------+-------------------------------+
| 9  | 23, 31 GHz :wave radiometer     | Integrated cloud liquid water |
|    | (ARM type - MAILBOX)            |                               |
|    |                                 | Integrated total water vapor  |
+----+---------------------------------+-------------------------------+
| 10 | BNL rotating shadowband         | Direct/diffuse solar          |
|    | radiometer                      |                               |
+----+---------------------------------+-------------------------------+
| 11 | 35 GHz Doppler cloud radar      | Cloud microphysical           |
|    |                                 | properties                    |
+----+---------------------------------+-------------------------------+
| 12 | 20, 31, 90 GHz :wave radiometer | Integrated cloud liquid water |
|    | (ESRL)                          |                               |
|    |                                 | Integrated total water vapor  |
+----+---------------------------------+-------------------------------+
| 13 | Upward pointed IR thermometer   | Cloud base radiative          |
|    |                                 | temperature                   |
+----+---------------------------------+-------------------------------+
| 14 | *Ronald H. Brown* C-band radar  | Precipitation spatial         |
|    |                                 | structure                     |
+----+---------------------------------+-------------------------------+

  ----------------- ------------ ----------- -------- ---------- ------------------
  **Table 2.**                                                   
  Standard                                                       
  deviation of                                                   
  selected                                                       
  variables,                                                     
  computed for all                                               
  three years of                                                 
  cruise data                                                    
  during each                                                    
  season (spring or                                              
  fall) within a                                                 
  single latitude                                                
  bin. The values                                                
  presented are the                                              
  average variance                                               
  of all latitude                                                
  bins.                                                          

  **Variable**      **Symbol**   **Units**   **Fall   **Spring   **Measurement
                                             σ**      σ**        Height**

  Wind speed        *S*          ms^-1^      2.5      2.5        17.5 (*RB*) / 16.5
                                                                 (*KA*) m

  N. wind component *U~n~*       ms^-1^      1.5      1.4        17.5 (*RB*) / 16.5
                                                                 (*KA*) m

  E. wind component *U~e~*       ms^-1^      2        2          17.5 (*RB*) / 16.5
                                                                 (*KA*) m

  Air temperature   *T~a~*       C           0.77     0.42       14.8 m

  SST               *T~s~*       C           0.92     0.57       -20 cm

  Specific humidity *q*          g kg^-1^    0.65     0.7        14.8 m

  Downward solar    *R~sd~*      Wm^-2^      56       27         N/A
  flux                                                           

  Downward IR flux  *R~ld~*      Wm^-2^      14       9.3        N/A

  Clear sky solar   *R~scl~*     Wm^-2^      14       14         \--
  flux                                                           

  Clear sky solar   *R~lcl~*     Wm^-2^      6.5      3.8        \--
  flux                                                           

  Sensible heat     *H~s~*       Wm^-2^      2.8      3.5        17.5 (*RB*) / 16.5
  flux                                                           (*KA*) m

  Latent heat flux  *H~l~*       Wm^-2^      24       20         17.5 (*RB*) / 16.5
                                                                 (*KA*) m

  Cloud fraction    *f*          \-          0.2      0.14       N/A

  Cloud base height *z~b~*       m           190      180        N/A

  Precipitable      *IV*         cm          0.89     0.61       N/A
  water                                                          
  ----------------- ------------ ----------- -------- ---------- ------------------

  -------------- -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  **Table 3a.**                                                                          
  Mean ESRL                                                                              
  observations                                                                           
  along 95 and                                                                           
  110 W                                                                                  
  longitude for                                                                          
  fall                                                                                   
  1999-2002.                                                                             

  lat            Deg      -8.0   -6.0   -4.0   -2.0   0.0    2.0    4.0    6.0    8.0    10.0

  Un             m s^-1^  -2.9   -3.6   -3.2   -2.7   -3.7   -5.4   -6.8   -5.9   -1.0   -0.2

  Ue             m s^-1^  5.9    5.3    5.1    2.7    3.7    4.2    0.8    0.3    2.6    -0.2

  Ts             C        22.8   22.2   22.4   21.9   21.8   24.4   26.0   27.0   27.5   28.6

  Ta             C        21.7   21.4   21.8   21.2   21.3   22.5   23.9   24.7   25.7   26.7

  qa             g kg^-1^ 12.1   13.4   13.1   13.8   13.7   13.9   15.2   16.3   17.8   18.0

  IV             cm       2.3    2.6    2.7    3.5    3.7    4.0    4.9    5.5    5.9    6.2

  rain           mm d^-1^ 0.1    0.4    0.2    0.2    0.2    0.2    1.3    0.6    13.5   8.1

  Hs             Wm^-2^   5.3    3.8    1.9    1.4    2.1    16.8   16.4   15.2   6.0    9.6

  Hl             Wm^-2^   109    89     70     37     32     121    128    114    69     89

  Rsd            Wm^-2^   313    267    271    273    262    243    218    173    207    252

  Rld            Wm^-2^   375    392    389    389    385    399    421    431    426    422

  Rlnet          Wm^-2^   -60    -41    -44    -42    -43    -40    -30    -25    -37    -50

  Rsclr          Wm^-2^   351    340    345    331    323    318    306    302    291    326

  Rlclr          Wm^-2^   348    362    354    355    357    366    380    395    404    412

  Zb 15%         m        887    893    941    674    888    780    809    825    951    730

  Zb peak        m        700    650    550    600    400    450    600    700    600    500

  cloud f                 0.42   0.58   0.57   0.49   0.58   0.69   0.84   0.89   0.58   0.25

  cloud_s f               0.31   0.59   0.52   0.56   0.61   0.62   0.72   0.91   0.50   0.27

  Trans_sol               0.89   0.79   0.79   0.83   0.81   0.76   0.71   0.57   0.71   0.77

  W              gm^-2^   32     58     33     39     44     54     74     106    176    60

  W_cld          gm^-2^   45     84     60     55     62     135    85     135    220    54

  W_cld_s        gm^-2^   35     89     50     39     48     159    81     144    222    60

  CFsl           Wm^-2^   -37    -69    -70    -55    -58    -71    -84    -123   -79    -70

  MCFsl          Wm^-2^   -118   -116   -133   -98    -94    -114   -117   -135   -160   -257

  CFir           Wm^-2^   26     30     33     32     27     32     40     35     21     10

  MCfir          Wm^-2^   63     51     58     67     46     47     47     40     37     39
  -------------- -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

  -------------- -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  **Table 3b.**                                                                          
  Mean ESRL                                                                              
  observations                                                                           
  along 95 and                                                                           
  110 W                                                                                  
  longitude for                                                                          
  spring                                                                                 
  2000-2002.                                                                             

  Lat            Deg      -8.0   -6.0   -4.0   -2.0   0.0    2.0    4.0    6.0    8.0    10.0

  Un             m s^-1^  -4.6   -4.6   -4.3   -1.9   -1.1   -0.1   1.3    2.7    3.0    1.2

  Ue             m s^-1^  7.2    5.9    4.1    2.5    2.3    0.7    1.1    3.8    5.5    5.0

  Ts             C        26.9   27.1   27.1   27.4   26.7   27.9   29.0   28.8   28.3   28.6

  Ta             C        25.9   26.4   26.0   26.5   25.9   26.8   27.7   27.3   27.5   27.7

  qa             g kg^-1^ 16.6   17.0   17.1   17.9   18.1   17.9   18.0   18.6   17.7   17.8

  IV             cm       4.0    5.0    5.0    5.6    5.3    5.6    5.4    5.2    4.1    4.2

  rain           mm d^-1^ 2.7    1.0    4.8    1.2    0.8    1.9    4.3    1.7    0.5    0.3

  Hs             Wm^-2^   7.3    5.2    6.6    4.2    3.1    4.9    6.1    7.6    3.5    4.1

  Hl             Wm^-2^   131    118    98     72     43     78     100    108    121    114

  Rsd            Wm^-2^   245    276    226    248    277    275    265    272    280    283

  Rld            Wm^-2^   418    415    431    423    416    427    422    422    411    418

  Rlnet          Wm^-2^   -39    -42    -29    -36    -40    -40    -48    -46    -54    -51

  Rsclr          Wm^-2^   297    312    308    310    319    319    319    322    326    328

  Rlclr          Wm^-2^   400    404    403    408    405    409    416    416    410    413

  Zb 15%         m        906    936    901    827    808    871    757    598    591    707

  Zb peak        m        600    600    600    500    350    450    600    600    500    600

  cloud f                 0.53   0.41   0.74   0.32   0.30   0.45   0.31   0.30   0.23   0.20

  cloud_s f               0.52   0.15   0.73   0.22   0.20   0.27   0.29   0.32   0.23   0.21

  Trans_sol               0.83   0.88   0.73   0.80   0.87   0.86   0.83   0.84   0.86   0.86

  W              gm^-2^   62     45     40     36     27     34     35     35     36     28

  W_cld          gm^-2^   124    123    67     137    37     73     70     64     123    38

  W_cld_s        gm^-2^   107    61     75     178    42     95     197    146    205    168

  CFsl           Wm^-2^   -49    -35    -78    -52    -50    -46    -52    -48    -44    -43

  MCFsl          Wm^-2^   -94    -225   -107   -236   -250   -156   -180   -149   -188   -202

  CFir           Wm^-2^   18     11     28     15     11     18     6      6      1      4

  MCfir          Wm^-2^   34     27     38     48     38     39     19     19     5      22
  -------------- -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

![](media/image1.png){width="6.408333333333333in"
height="6.500694444444444in"}

Figure 1. The EPIC mooring array is shown in relation to the September
1998 TRMM Microwave Imager SST and QuickSCAT wind fields. Diamonds
indicate the locations of TAO buoys.

![](media/image2.png){width="5.621527777777778in"
height="4.0152777777777775in"}

![](media/image3.png){width="5.621527777777778in"
height="4.020833333333333in"}

Figure 2. One-month rainfall accumulations from the TRMM 3B42 rainfall
product for spring (a) and fall (b) 2000 cruises.

![](media/image4.jpeg){width="5.247916666666667in"
height="3.4611111111111112in"}

![](media/image5.jpeg){width="5.375in" height="4.03125in"}

Figure 3. Wind components (northerly --- upper panel; easterly --- lower
panel) from seven EPIC cruises. (a) Daily-averaged values, with each
symbol representing a single cruise and includes both 110W and 95W
longitudinal portions. (b) Latitude average values. Circle --- fall;
diamond --- spring.

![](media/image6.jpeg){width="5.125in" height="3.84375in"}Figure 4.
Latitude-averaged turbulent heat fluxes. Upper panel --- sensible heat;
lower panel --- latent heat. Circles --- fall; diamonds --- spring.

![](media/image7.jpeg){width="5.25in" height="3.9375in"}

Figure 5. Latitude-averaged net radiative heat fluxes. Upper panel ---
solar flux; lower panel --- IR flux. Circles --- fall; diamonds ---
spring.

![](media/image8.jpeg){width="6.0in" height="4.5in"}

Figure 6. Latitude-averaged low cloud fraction. Circles --- fall;
diamonds --- spring. Solid line --- 24-hr average cloud fraction; dashed
line --- daytime average cloud fraction.

Figure 7. Latitude-averaged net clear-sky radiative heat fluxes using
the models described in Appendix A. Upper panel --- solar flux; lower
panel --- IR flux; circles --- fall; diamonds --- spring. The strange
bump at 10N in the fall is caused by observations in the EPIC2001
project which occurred almost two months earlier than the typical fall
monitoring cruise.

![](media/image9.jpeg){width="5.125in" height="3.84375in"}

![](media/image10.jpeg){width="5.125in" height="3.84375in"}

Figure 8. Latitude-averaged radiative surface SFC. x --- fall IR flux;
closed circles --- spring IR flux; open circles --- fall solar flux;
diamonds --- spring solar flux.

![](media/image11.jpeg){width="5.125in" height="3.84375in"}

Figure 9. Latitude-averaged radiative surface maximum SFC computed via
Eq. 2. x --- fall IR flux; closed circles --- spring IR flux; open
circles --- fall solar flux; diamonds --- spring solar flux.

![](media/image12.jpeg){width="5.125in" height="3.84375in"}

Figure 10. Cloud forcing phase diagram showing the balance of solar and
IR SCF in different climate zones. An ensemble of individual days in a
particular region tend to lie along a line where values near zero from
small cloud fraction and values near the maximum are for cloud fraction
near 1.0. Symbols are for the data from this analysis: Circle --- fall;
x --- spring.

![](media/image13.jpeg){width="5.25in" height="3.9375in"}Figure 11.
Daily-averaged SFC as a function of cloud fraction. Circle --- IR; x ---
solar. The solid lines represent nominal MSFC values of 50 Wm^-2^ for IR
and -150 Wm^-2^ for solar fluxes.

![](media/image14.jpeg){width="5.25in" height="3.9375in"}Figure 12. As
in Figure 11, but the daily data have been averaged in bins of cloud
fraction. Circles --- spring IR; plus --- fall IR; diamonds --- spring
solar; x --- fall solar.

![](media/image15.jpeg){width="3.75in" height="2.8125in"}

Figure 13. Cloud forcing as a function of latitude averaged of the
spring and fall PACS cruises near the equator in the East Pacific: a)
Fall Solar SCF, b) Fall IR SCF, c) Spring Solar SCF, and d) Spring IR
SCF. The symbols for measurements are: TAO buoys -- circle, ESRL ship
data -- square, ISCCP -- right pointing open triangle, mean of these
three -- heavy solid line. The symbols for the reanalysis products are:
NCEP2 -- left pointing triangle, and ERA40 -- star. ESRL data are
computed from cruise averages; buoy, satellite, and NWP are computed
from monthly averages in the cruise season window.

![](media/image19.jpeg){width="5.9944444444444445in"
height="3.9451388888888888in"}

Figure 14. Latitude-averaged surface fluxes. Upper panel --- net heat
flux into the ocean; lower panel --- contribution to the net heat flux
accounted for by SCF. Circles ---fall; diamonds --- spring.

![](media/image20.jpeg){width="6.0in" height="4.5in"}

Figure A1. Comparison of clear-sky IR fluxes using model (x-axis) from
Eqs. 4--5 and measurements (y-axis) for 60-min cloud free periods.